Machine Learning Jobs

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what's happening now and shape what's coming next. Vantor is a place for problem solvers, changemakers, and go-getters-where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world. To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee. Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3). Please review the job details below. Vantor is seeking a Senior HRIS Analyst, Talent Management & Learning Systems to support the configuration, maintenance, and continuous improvement of HR technology tied to talent management, learning, employee development, and performance management processes. This role serves as a subject matter expert for Workday Talent & Performance, Learning, and Journeys functionality, partnering with HR, People Operations, Talent Management COE, and business stakeholders to ensure employee lifecycle programs are supported through effective systems, automation, reporting, and analytics. This role will help build & configure performance review cycles, talent calibration processes, learning campaigns, compliance training, employee journeys, and related reporting needs. This position plays an important role in improving HR service delivery, strengthening data integrity, and enhancing the employee and stakeholder experience across Workday-supported programs. Responsibilities - Configure, administer, and maintain Workday Talent processes, including performance review cycles, talent assessments, calibration workflows, and related system processes. - Design, implement, and support specialized talent programs, employee development initiatives, and review processes aligned with organizational goals, business requirements, compliance needs, and process standards. - Serve as a primary administrator for Workday Learning functionality. - Configure, launch, monitor, and maintain learning campaigns, compliance training programs, onboarding training, role-based training, and employee development initiatives. - Administer and maintain Workday Journeys, including journey content, workflows, security roles, distribution rules, and lifecycle experience automation. - Ensure journey content remains current, accurate, and aligned with business processes and employee lifecycle needs. - Provide Tier 2 HRIS support for Workday-related inquiries, issues, configuration challenges, and service cases. - Identify opportunities to automate HR processes, improve user experience, and d eploy AI-enabled solutions to enhance HR operations and service delivery. - Investigate, troubleshoot, and resolve complex issues related to talent management, learning, reporting, core HCM functionality, and user experience. - Develop, maintain, and troubleshoot Workday reports and dashboards that support HR leaders, People Operations, and business stakeholders. - Analyze HR data to identify trends, improve processes, support decision-making, and maintain data accuracy across talent, learning, and employee information systems. - Evaluate Workday semi-annual release functionality and make recommendations for feature adoption. - Participate in testing, implementation, and deployment of new HRIS functionality, releases, enhancements, and process improvements. - Document system configurations, procedures, training resources, and best practices. Minimum Qualifications - Bachelor's degree in Human Resources, Information Systems, Business Administration, or a related field; or equivalent combination of education and experience. - 3-5 years of Workday HCM experience, along with Workday Talent & Performance, Workday Learning, and/or Workday Journeys. - Experience supporting talent management, performance management, learning management, employee development systems, or related HR processes. - Experience creating, maintaining, and troubleshooting reports and dashboards. - Strong analytical, problem-solving, and troubleshooting skills. - Ability to manage multiple priorities in a fast-paced environment. - Strong communication, documentation, and stakeholder partnership skills. - Ability to maintain confidentiality and accuracy when working with employee data and HR systems. Preferred Qualifications - HR case management, shared services, or Tier 2 HRIS support experience. - Experience with Workday Talent Marketplace and Workday Succession Planning. - Experience configuring HR workflows, business processes, campaigns, or employee lifecycle programs. - Experience supporting performance review cycles, talent calibration, compliance training, or employee development programs. - Experience with AI-enabled productivity, automation, reporting, or analytics tools. - Workday certifications or advanced Workday training. Pay Transparency: To support pay transparency, Vantor includes salary ranges in all U.S. job postings. Starting pay for this role will fall within the listed range and will be based on factors such as experience, qualifications, skills, location, and market conditions. Candidates who meet the minimum requirements for the role should not expect to receive compensation at the top of the range. The listed range reflects the expected pay for this position, and final offers will be determined based on each candidate's experience, expertise, and alignment with the role. The base pay for this position within Colorado is: $83,000.00 - $110,000.00 - $121,000.00 annually. For all other states, we use geographic cost of labor as an input to develop market-driven ranges for our roles, and as such, each location where we hire may have a different range. Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption reimbursement and pet insurance to support all aspects of your life. You can find more information on our benefits at: Additionally, this position is incentive eligible with a target based on contribution, company performance, and/or individual results achieved; the specific incentive plan and target amount will be determined based on the role and breadth of contributions. The application window is three days from the date the job is posted and will remain posted until a qualified candidate has been identified for hire. If the job is reposted regardless of reason, it will remain posted three days from the date the job is reposted and will remain reposted until a qualified candidate has been identified for hire. The date of posting can be found on Vantor's Career page at the top of each job posting. To apply, submit your application via Vantor's Career page. EEO Policy: Vantor is an equal opportunity employer committed to an inclusive workplace. We believe in fostering an environment where all team members feel respected, valued, and encouraged to share their ideas. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability, protected veteran status, age, or any other characteristic protected by law.

Senior Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Plano, TX: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Manager IC) As a Capital One Machine Learning Engineer (MLE), you'll join an Agile team building and productionizing foundation models at scale. Our work centers on self-supervised learning for transformer architectures - pretraining on Capital One's rich, large-scale behavioral data to learn representations that power applications across use cases such as fraud, marketing, and servicing . You'll participate in the detailed technical design, development, and implementation of these systems, spanning model architecture, large-scale training and representation learning, and the engineering required to serve models reliably in production. You'll develop and review model and application code, drive machine learning architectural decisions, and ensure the high availability and performance of our applications. And you'll have the opportunity to continuously learn and apply the latest innovations in self-supervised learning, transformer modeling, and ML engineering best practices. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications - Retrain, maintain, and monitor models in production - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI - Use programming languages like Python, Scala, or Java Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Senior Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Plano, TX: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Distinguished Machine Learning Engineer Overview: As a Capital One Machine Learning Engineer, you'll be providing technical leadership to engineering teams dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll serve as a technical domain expert in machine learning, guiding machine learning architectural design decisions, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. You'll also mentor other engineers and further develop your technical knowledge and skills to keep Capital One at the cutting edge of technology. What you'll do in the role: - Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams - Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems - Lead large-scale ML initiatives with the customer in mind - Leverage cloud-based architectures and technologies to deliver optimized ML models at scale - Optimize data pipelines to feed ML models - Use programming languages like Python, Scala, C/C++ - Leverage compute technologies such as Dask and RAPIDS - Evangelize best practices in all aspects of the engineering and modeling lifecycles - Help recruit, nurture, and retain top engineering talent Basic Qualifications: - Bachelor's degree - At least 10 years of experience designing and building data-intensive solutions using distributed computing - At least 7 years of experience programming in C, C++, Python, or Scala - At least 4 years of experience with the full ML development lifecycle using modern technology in a business critical setting Preferred Qualifications: - Master's Degree - 3+ years of experience designing, implementing, and scaling production-ready data pipelines that feed ML models - 3+ years of experience using Dask, RAPIDS, or in High Performance Computing - 3+ years of experience with the PyData ecosystem (NumPy, Pandas, and Scikit-learn) - Ability to communicate complex technical concepts clearly to a variety of audiences - ML industry impact through conference presentations, papers, blog posts, or open source contributions - Ability to attract and develop high-performing software engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr Distinguished Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Manager IC) At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent along with our deep experience in machine learning position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers. - Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI. - Fine-tune, develop and evaluate machine learning and foundation models, - Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities - Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One. - Leverage a broad stack of Open Source and SaaS AI technologies. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues. - Retrain, maintain, and monitor models in production. - Construct optimized data pipelines to feed ML models. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. The Ideal Candidate: - You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. - You are a passionate communicator, comfortable with explaining complex technical concepts to non-technical partners across the business, sometimes in front of large audiences. - Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. - You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. - You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. - You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. - Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production Strategic & Business-Oriented: think beyond the technology, deeply understanding business needs and how AI can solve them. You don't just build; you strategize and prioritize work that delivers the greatest business value. Highly Collaborative & Transparent: You are a natural partner, working seamlessly across engineering, product, and data science teams. You communicate your progress, blockers, and decisions clearly and proactively, ensuring everyone is aligned. You love sharing knowledge and insights and are committed to the success of the entire team. Technically Mature & Humble: You possess a strong foundation in engineering and mathematics. You are a resilient problem-solver who can bring clarity to complex, undefined problems and articulate your findings concisely. You demonstrate professional maturity by committing to and executing on team decisions. Flexible & Fungible: You are eager to roll up your sleeves and contribute wherever the team needs you most. You are comfortable working across different aspects of the tech stack and adapting to evolving priorities. A Lifelong Learner: You love staying current with the latest AI research and can apply novel techniques to production systems judiciously, always with a focus on business impact. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 7+ years of experience designing, developing, delivering, and supporting AI services at scale - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing AI and ML algorithms or technologies using Python - 2+ years of experience with Retrieval Augmented Generation (RAG) - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion - Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace . click apply for full job details

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. What you'll do in the role: - The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Senior Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Plano, TX: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications - Retrain, maintain, and monitor models in production - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI - Use programming languages like Python, Scala, or Java Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer San Francisco, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. This specialized machine learning engineering group is responsible for the decisioning technology that guides customers through their entire credit journey. Our team standardizes and streamlines how complex machine learning models are built, deployed, and monitored at scale, drastically reducing friction for our data science partners. Utilizing a modern technology stack focused heavily on Python and Kubernetes, you will create high-impact infrastructure that supports millions of customers in real time from their initial application throughout their entire lifecycle. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications - Retrain, maintain, and monitor models in production - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI - Use programming languages like Python, Scala, or Java Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Lead Machine Learning Engineer (IC) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications - Retrain, maintain, and monitor models in production - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI - Use programming languages like Python, Scala, or Java Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or XGboost - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Senior Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - 3+ years of people management experience - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Plano, TX: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer (Manager IC) At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent along with our deep experience in machine learning position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers. - Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI. - Fine-tune, develop and evaluate machine learning and foundation models, - Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities - Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One. - Leverage a broad stack of Open Source and SaaS AI technologies. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues. - Retrain, maintain, and monitor models in production. - Construct optimized data pipelines to feed ML models. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. The Ideal Candidate: - You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. - You are a passionate communicator, comfortable with explaining complex technical concepts to non-technical partners across the business, sometimes in front of large audiences. - Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. - You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. - You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. - You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. - Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production Strategic & Business-Oriented: think beyond the technology, deeply understanding business needs and how AI can solve them. You don't just build; you strategize and prioritize work that delivers the greatest business value. Highly Collaborative & Transparent: You are a natural partner, working seamlessly across engineering, product, and data science teams. You communicate your progress, blockers, and decisions clearly and proactively, ensuring everyone is aligned. You love sharing knowledge and insights and are committed to the success of the entire team. Technically Mature & Humble: You possess a strong foundation in engineering and mathematics. You are a resilient problem-solver who can bring clarity to complex, undefined problems and articulate your findings concisely. You demonstrate professional maturity by committing to and executing on team decisions. Flexible & Fungible: You are eager to roll up your sleeves and contribute wherever the team needs you most. You are comfortable working across different aspects of the tech stack and adapting to evolving priorities. A Lifelong Learner: You love staying current with the latest AI research and can apply novel techniques to production systems judiciously, always with a focus on business impact. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 7+ years of experience designing, developing, delivering, and supporting AI services at scale - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing AI and ML algorithms or technologies using Python - 2+ years of experience with Retrieval Augmented Generation (RAG) - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion - Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace . click apply for full job details

Lead Machine Learning Engineer (Enterprise Platforms Technology) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: - The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer San Francisco, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer San Jose, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). - Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. - Retrain, maintain, and monitor models in production. - Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor's Degree - At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 3 years of experience building, scaling, and optimizing ML systems - At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, Kubeflow or TensorFlow - 3+ years of experience developing performant, resilient, and maintainable code - 3+ years of experience with data gathering and preparation for ML models - ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents - 3+ years of experience building production-ready data pipelines that feed ML models - Ability to communicate complex technical concepts clearly to a variety of audiences Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer San Francisco, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer San Jose, CA: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine exceptional products for our customers. In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: - Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers. - Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability and agentic AI. - Fine-tune, develop and evaluate machine learning and foundation models, - Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities - Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One. - Leverage a broad stack of Open Source and SaaS AI technologies. - Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues. - Retrain, maintain, and monitor models in production. - Construct optimized data pipelines to feed ML models. - Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. The Ideal Candidate: - You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. - You are a passionate communicator, comfortable with explaining complex technical concepts to non-technical partners across the business, sometimes in front of large audiences. - Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. - You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. - You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. - You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. - Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production Strategic & Business-Oriented: You think beyond the technology, deeply understanding business needs and how AI can solve them. You don't just build; you strategize and prioritize work that delivers the greatest business value. Highly Collaborative & Transparent: You are a natural partner, working seamlessly across engineering, product, and data science teams. You communicate your progress, blockers, and decisions clearly and proactively, ensuring everyone is aligned. You love sharing knowledge and insights and are committed to the success of the entire team. Technically Mature & Humble: You possess a strong foundation in engineering and mathematics. You are a resilient problem-solver who can bring clarity to complex, undefined problems and articulate your findings concisely. You demonstrate professional maturity by committing to and executing on team decisions. Flexible & Fungible: You are eager to roll up your sleeves and contribute wherever the team needs you most. You are comfortable working across different aspects of the tech stack and adapting to evolving priorities. A Lifelong Learner: You love staying current with the latest AI research and can apply novel techniques to production systems judiciously, always with a focus on business impact. Basic Qualifications: - Bachelor's Degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field - 7+ years of experience designing, developing, delivering, and supporting AI services at scale - 3+ years of experience building production-ready data pipelines that feed ML models - 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 3+ years of experience developing AI and ML algorithms or technologies using Python - 2+ years of experience with Retrieval Augmented Generation (RAG) - 2+ years of experience with data gathering and preparation for ML models - 2+ years of people leader experience - 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion - Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace . click apply for full job details

Location Marlborough, MassachusettsJob Type: ContractCompensation Range: $30 - 33 per hourAre you ready to transform complex regulatory strategies into highly engaging, enterprise-scale learning solutions? A leading global enterprise is seeking a consultative partner to architect and modernize end-to-end compliance learning experiences across diverse modalities. In this role, you will lead the strategic development of high-impact training programs that elevate ethical decision-making and drive measurable business compliance. Responsibilities Architect multimodal eLearning and microlearning initiatives to accelerate enterprise-wide policy adoption and ethical compliance.Direct end-to-end instructional design processes-from storyboarding to multimedia production-to optimize learner engagement across platforms.Consult with cross-functional executive stakeholders and SMEs to translate complex legal and regulatory frameworks into actionable learning assets.Drive localized, scalable learning solutions across international divisions to maintain continuous compliance standard alignment. Qualifications - 2+ years of strategic instructional design experience specializing in corporate learning environments.Specialized expertise in eLearning authoring tools, LMS platforms, video production, and localized content delivery.Proven track record of managing multi-channel learning projects supporting risk, compliance, or corporate policy initiatives.Bachelor's degree in Instructional Design or Education, paired with exceptional stakeholder management abilities.JOBID: 98 Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.At Cella, a randstad digital company, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. - If you require a reasonable accommodation to make your application or interview experience a great one, please contact offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. - In addition, Cella by randstad digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility). - This posting is open for thirty (30) days.It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. - An employer who violates this law shall be subject to criminal penalties and civil liability.

Description: Monoflex Machine Setup Operator Department: Manufacturing Reports To: Manufacturing Supervisor / Plant Manager Status: Full-Time Pay: Starting at $20.00 per hour Hours: Monday-Friday 6:30-3 pm Monoflex is a leading manufacturer of flexible drop pipe and water system products serving customers across North America. Our team takes pride in producing high-quality products that support residential, agricultural, municipal, and industrial water systems. As a Machine Setup Operator, you will play a key role in manufacturing operations by setting up, operating, and monitoring production equipment while ensuring product quality, safety, and efficiency. Benefits - Starting pay of $20.00 per hour - Medical, Dental, and Vision Insurance - Company-Paid Life Insurance - Company-Paid Short-Term Disability Insurance - Company-Paid Long-Term Disability Insurance - 401(k) with Company Match - Paid Time Off (PTO) - Paid Holidays - Employee Assistance Program (EAP) - Training and Career Growth Opportunities Position Summary The Machine Setup Operator is responsible for setting up and operating lathes, slotting machines, and related manufacturing equipment to produce quality parts that meet customer specifications. This position requires mechanical aptitude, attention to detail, and the ability to read blueprints and use precision measuring instruments. Essential Responsibilities Machine Setup & Operation - Set up and operate lathes, slotting machines, and related production equipment. - Read blueprints, work orders, and specifications to determine machining requirements. - Select, position, and secure tooling, fixtures, and workpieces. - Set machine feeds, speeds, and cutting depths according to specifications. - Operate machine controls and feeding devices to perform production operations. - Adjust machine settings as necessary to maintain quality and efficiency. Quality & Inspection - Verify product dimensions and tolerances using calipers, micrometers, gauges, and other measuring tools. - Monitor machine operation and inspect parts throughout the production process. - Identify and report quality concerns, equipment issues, or production discrepancies to supervision. - Maintain compliance with quality standards and work instructions. Safety & Housekeeping - Follow all company safety policies and procedures. - Wear required personal protective equipment (PPE). - Maintain a clean, organized, and safe work environment. - Practice safe operation of machinery and material handling equipment. Other Duties as Assigned Qualifications Required - High School Diploma or GED. - Ability to work in a manufacturing environment. - Mechanical aptitude and attention to detail. - Ability to read and follow work instructions and production documents. - Basic math and measurement skills. Preferred - Previous machining, machine operation, manufacturing, or setup experience. - Experience reading blueprints and technical drawings. - Experience using precision measuring instruments such as calipers and micrometers. - Forklift experience or ability to obtain certification. Skills & Abilities - Ability to troubleshoot minor machine issues. - Strong attention to quality and accuracy. - Ability to work independently and as part of a team. - Strong communication and organizational skills. - Ability to meet production goals while maintaining quality standards. Certifications - Forklift certification preferred or ability to obtain certification upon hire. Physical Requirements Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position. - Regular standing throughout the workday. - Frequent use of hands, tools, controls, and measuring devices. - Frequent lifting of up to 10 pounds. - Occasional lifting and moving of up to 50 pounds. - Ability to visually inspect products and equipment, including close vision, depth perception, and color recognition. Work Environment This position works in a manufacturing environment and may be exposed to: - Moving mechanical equipment and machinery. - Metalworking fluids, fumes, and airborne particles. - Moderate to loud noise levels. - Production equipment and material handling devices. Appropriate training and personal protective equipment will be provided. Equal Employment Opportunity Monoflex is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other protected status under applicable law. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position. Performance & Training Performance, competency, and training requirements will be evaluated during onboarding, periodic reviews, and annual performance evaluations in accordance with company policies. Employees will receive job-specific training and ongoing development to support success in the role. Requirements: PIf3caa-9017

12 Month FTC Would you like to kick startyour career in a supportive,collaborativeand innovative company? Do you enjoy working as part of an enthusiastic, passionate,and collaborative team? Join our Learning & Development Team Softcat's Learning & Development team sits within a wider People & Property department consisting of People Operations, People Business Partners, Reward, Talent Acquisition, click apply for full job details

Assessment & Learning Design ManagerRole Purpose The Assessment & Learning Design Manager will lead the design, quality assurance and effectiveness of integrated learning and assessment products for Schools, Higher Education and Workplace audiences. The role holder will design, develop, maintain and continuously improve assessment and learning content to achieve desired pedagogical outcomes, meet regulatory and quality standards, and deliver value to customers. The role will also develop, maintain and share expertise on the pedagogical design and effectiveness of learning and assessment products with internal and external stakeholders. Role Overview This role sits within a team responsible for the design, development and performance management of digital and non-digital learning and assessment products. The team ensures the right product mix, quality standards and innovation to support strategic growth objectives. Products must be commercially viable, scalable and aligned with technology, delivery and customer needs. Key ResponsibilitiesLearning & Assessment Content Design - Collaborate with Research, Product, Proposition, Portfolio and Content teams to design and develop curricula and assessments aligned to global and national standards and best practice. - Ensure learning and assessment products are valid, reliable and capable of generating meaningful and actionable learner outcomes. - Implement quality assurance frameworks and assessment item development standards to maintain accuracy, relevance and effectiveness. - Design and implement policies and processes relating to accommodations and accessibility requirements where appropriate. - Ensure compliance with relevant regulatory requirements, accessibility standards, EDI principles, safeguarding requirements and AI policies. - Contribute expertise to initiatives involving automation and AI tools for content generation, calibration, archiving and reuse. - Develop project guidelines, style guides, specifications and scoring frameworks for internal teams and external suppliers. - Create comprehensive documentation to support handover of content designs into production. Stakeholder Management - Work closely with Marketing, Product, Proposition, Research, Content Creation and Content Management teams to ensure alignment with customer, market and business needs. - Gather and incorporate feedback from learners, educators, consultants and stakeholders to improve learning and assessment experiences. - Commission or support research into best practice in learning design, assessment design and psychometrics to ensure products remain innovative and effective. Project Management - Plan and prioritise work effectively to ensure successful delivery of projects and programmes. - Balance quality, compliance, budget and timelines throughout project delivery. - Manage resources efficiently within agreed financial parameters. - Support monitoring and evaluation activities to maintain compliance and quality standards. - Resolve issues relating to assessment development, content quality, compliance requirements and third-party suppliers. Data Analysis & Problem Solving - Validate learning and assessment solutions through piloting, pre-testing, user testing and sampling activities. - Design assessment and learning approaches that minimise bias through robust data collection and analysis methodologies. - Use performance data and analytics to evaluate product effectiveness and recommend improvements. Required Skills & ExperienceEssential - Extensive Experience in English Language Teaching (ELT) learning design and/or English language assessment development. - Experience designing learning products, assessments or qualifications for English language learners. - Experience in product development and/or product management, including digital and online learning products. - Experience using data, analytics and assessment results to improve learning and assessment outcomes. - Experience managing test piloting, seeding and pre-testing activities. - Experience working within a global or international organisation. - Strong stakeholder management and communication skills. Desirable - Understanding of accessibility and EDI requirements in learning and assessment. - Experience using Agile methodologies. - Expertise in specialist areas such as AI in education, employability, higher education or workplace learning. - Familiarity with regulatory frameworks relevant to educational assessment (e.g. Ofqual). EducationEssential - Higher qualification in English Language Teaching, Assessment, Applied Linguistics or a related field. - Examples include: - Master's degree - PhD - DELTA - DipTESOL - Equivalent advanced professional qualification Job Title: Assessment & Learning Design Manager Location: Stratford, UK Job Type: Permanent Trading as Aston Carter. Allegis Group Limited, Maxis 2, Western Road, Bracknell, RG12 1RT, United Kingdom. No. (phone number removed). Allegis Group Limited operates as an Employment Business and Employment Agency as set out in the Conduct of Employment Agencies and Employment Businesses Regulations 2003. Aston Carter is a company within the Allegis Group network of companies (collectively referred to as "Allegis Group"). Aerotek, Aston Carter, EASi, Talentis Solutions, TEKsystems, Stamford Consultants and The Stamford Group are Allegis Group brands. If you apply, your personal data will be processed as described in the Allegis Group Online Privacy Notice available at (url removed)> To access our Online Privacy Notice, which explains what information we may collect, use, share, and store about you, and describes your rights and choices about this, please go to (url removed)> We are part of a global network of companies and as a result, the personal data you provide will be shared within Allegis Group and transferred and processed outside the UK, Switzerland and European Economic Area subject to the protections described in the Allegis Group Online Privacy Notice. We store personal data in the UK, EEA, Switzerland and the USA. If you would like to exercise your privacy rights, please visit the "Contacting Us" section of our Online Privacy Notice at (url removed)/en-gb/privacy-notices for details on how to contact us. To protect your privacy and security, we may take steps to verify your identity, such as a password and user ID if there is an account associated with your request, or identifying information such as your address or date of birth, before proceeding with your request. If you are resident in the UK, EEA or Switzerland, we will process any access request you make in accordance with our commitments under the UK Data Protection Act, EU-U.S. Privacy Shield or the Swiss-U.S. Privacy Shield.

Our client based in Longton is a well established and growing manufacturing business based inLongton, specialising in high-quality metal cutting, bending and welding solutions for a diverse range of customers. Due to continued growth, they are looking for an experiencedCNC Programmerto join their machining team on a full-time, permanent basis click apply for full job details

Do you want to make change happen by joining a team that never stands still? Do you enjoy working as part of an enthusiastic, passionate, and collaborative team? Join our Learning & Development Team Softcat's Learning & Development team sits within a wider People & Property department consisting of People Operations, People Business Partners, Reward, Talent Acquisition, Diversity & Inclusion and Employee Engagement. The purpose of the L&D team is to champion a learning culture that empowers employees to reach their potential and drive high performance at Softcat. The L&D team plays an influential part in our business, ensuring that Softcat succeeds through our people Success. The Softcat Way. It's an exciting time to be at Softcat, one of the UK's most successful technology solutions businesses. We help customers to use technology to succeed, by putting our employees first. We've reached the £1 billion+ pa revenue milestone, opened our first office outside the UK and picked up a series of industry awards. We've got even bigger plans for the future. So, if you share our drive and ambition, get ready to achieve more from your career. Enabling Sales Excellence through strategic learning and development As a Learning & Development Consultant, you'll partner with senior leaders and sales teams to identify capability needs, improve performance and deliver learning solutions that drive measurable business outcomes. Using a performance consulting approach, you'll uncover root causes, shape capability strategies and create targeted upskilling plans aligned to business goals. You'll build trusted relationships, influence stakeholders and measure the impact of learning to ensure lasting performance improvement. As a Learning & Development Consultant, you'll be responsible for: - Building trusted relationships with senior leaders and sales teams, acting as a strategic advisor on capability and performance - Identifying capability gaps and creating targeted upskilling strategies that drive sales performance and business outcomes - Delivering impactful facilitation, coaching and learning solutions that build skills, confidence and capability - Leading end to end capability programmes, ensuring successful delivery and measurable results - Influencing stakeholders and shaping capability decisions through a performance consulting approach - Managing internal resources and external partners to maximise impact, value and business performance We'd love you to have - Demonstrating exceptional stakeholder management skills, with the ability to build credibility, influence and challenge constructively at all levels - Applying strong commercial awareness and performance consulting expertise to identify capability needs and deliver effective business-focused solutions - Creating and owning capability strategies, upskilling plans and development initiatives that drive measurable performance outcomes - Facilitating engaging learning experiences and providing coaching across virtual and face to face environments to support capability growth and behaviour change - Measuring impact through data and insights, using evaluation outcomes to continuously improve learning solutions and capability decisions - Managing multiple capability programmes, stakeholders and external partners, while balancing business priorities and delivering high quality results in a fast-paced commercial environment We also acknowledge that the confidence gap and imposter syndrome are a real thing and can get in the way of us meeting fantastic talent, so please don't hesitate to apply - we would love to hear from you! Work in a way that works for you We recognise that everyone is different and that the way in which people want to work and deliver at their best is different for everyone too. In this role, we can offer the following flexible working patterns: - Hybrid working - 3 days in the office and 2 days working from home - Working flexible hours - flexing the times you start and finish during the day - Flexibility around school pick up and drop offs Working with us Wherever you work, we want you to experience the freedom and autonomy to realise your potential. You will feel supported by a team that celebrates individuality, encourages different perspectives, and embraces every background. Join us To become part of the success story, please apply now. If you have a disability or neurodiversity, we can provide support or adjustments that you may need throughout our recruitment process or any mitigating circumstance you wish for us to consider. Any information you share on your application will be treated in confidence. You can find out more about life at Softcat and our commitments to diversity and inclusion at Here at Softcat, we don't prohibit the use of AI (artificial intelligence) in our application process, as we understand how far it can go to creating a truly equitable candidate experience. That being said, as a culture-driven organisation, we believe that the genuine essence of each person is what truly matters, so we highly encourage you to be as authentically you as possible when submitting your application to showcase your true and whole self.

Do you want to make change happen by joining a team that never stands still? Do you enjoy working as part of an enthusiastic, passionate, and collaborative team? Join our Learning & Development Team Softcat's Learning & Development team sits within a wider People & Property department consisting of People Operations, People Business Partners, Reward, Talent Acquisition, Diversity & Inclusion and Employee Engagement. The purpose of the L&D team is to champion a learning culture that empowers employees to reach their potential and drive high performance at Softcat. The L&D team plays an influential part in our business, ensuring that Softcat succeeds through our people Success. The Softcat Way. It's an exciting time to be at Softcat, one of the UK's most successful technology solutions businesses. We help customers to use technology to succeed, by putting our employees first. We've reached the £1 billion+ pa revenue milestone, opened our first office outside the UK and picked up a series of industry awards. We've got even bigger plans for the future. So, if you share our drive and ambition, get ready to achieve more from your career. Enabling Sales Excellence through strategic learning and development As a Learning & Development Consultant, you'll partner with senior leaders and sales teams to identify capability needs, improve performance and deliver learning solutions that drive measurable business outcomes. Using a performance consulting approach, you'll uncover root causes, shape capability strategies and create targeted upskilling plans aligned to business goals. You'll build trusted relationships, influence stakeholders and measure the impact of learning to ensure lasting performance improvement. As a Learning & Development Consultant, you'll be responsible for: - Building trusted relationships with senior leaders and sales teams, acting as a strategic advisor on capability and performance - Identifying capability gaps and creating targeted upskilling strategies that drive sales performance and business outcomes - Delivering impactful facilitation, coaching and learning solutions that build skills, confidence and capability - Leading end to end capability programmes, ensuring successful delivery and measurable results - Influencing stakeholders and shaping capability decisions through a performance consulting approach - Managing internal resources and external partners to maximise impact, value and business performance We'd love you to have - Demonstrating exceptional stakeholder management skills, with the ability to build credibility, influence and challenge constructively at all levels - Applying strong commercial awareness and performance consulting expertise to identify capability needs and deliver effective business-focused solutions - Creating and owning capability strategies, upskilling plans and development initiatives that drive measurable performance outcomes - Facilitating engaging learning experiences and providing coaching across virtual and face to face environments to support capability growth and behaviour change - Measuring impact through data and insights, using evaluation outcomes to continuously improve learning solutions and capability decisions - Managing multiple capability programmes, stakeholders and external partners, while balancing business priorities and delivering high quality results in a fast-paced commercial environment We also acknowledge that the confidence gap and imposter syndrome are a real thing and can get in the way of us meeting fantastic talent, so please don't hesitate to apply - we would love to hear from you! Work in a way that works for you We recognise that everyone is different and that the way in which people want to work and deliver at their best is different for everyone too. In this role, we can offer the following flexible working patterns: - Hybrid working - 3 days in the office and 2 days working from home - Working flexible hours - flexing the times you start and finish during the day - Flexibility around school pick up and drop offs Working with us Wherever you work, we want you to experience the freedom and autonomy to realise your potential. You will feel supported by a team that celebrates individuality, encourages different perspectives, and embraces every background. Join us To become part of the success story, please apply now. If you have a disability or neurodiversity, we can provide support or adjustments that you may need throughout our recruitment process or any mitigating circumstance you wish for us to consider. Any information you share on your application will be treated in confidence. You can find out more about life at Softcat and our commitments to diversity and inclusion at Here at Softcat, we don't prohibit the use of AI (artificial intelligence) in our application process, as we understand how far it can go to creating a truly equitable candidate experience. That being said, as a culture-driven organisation, we believe that the genuine essence of each person is what truly matters, so we highly encourage you to be as authentically you as possible when submitting your application to showcase your true and whole self.