Note: Candidates with Green Card, Green Card EAD, H1B, or US Citizen status are NOT eligible for this role. Job Title: Machine Learning Engineer / AI Engineer Location: Reston, VA or Plano, TX Contact email: Job Overview We are seeking a highly skilled Machine Learning Engineer / AI Engineer to design, develop, and deploy scalable machine learning and deep learning solutions. The ideal candidate will have strong programming expertise, hands-on experience with AI frameworks, and the ability to work across the full machine learning lifecycle. Key Responsibilities Develop, train, and optimize machine learning and deep learning models using frameworks such as TensorFlow and PyTorch. Build and maintain end-to-end AI/ML pipelines from data ingestion to model deployment. Analyze complex datasets to generate insights and improve model performance. Design and implement scalable AI solutions to solve business problems. Deploy machine learning models on cloud platforms such as Amazon Web Services (AWS). Work within CI/CD environments to ensure seamless model integration and deployment. Collaborate with cross-functional teams to identify and deliver AI-driven solutions. Required Qualifications Strong programming skills in Python (preferred), R, or Java Hands-on experience with machine learning algorithms and frameworks Experience with cloud platforms, preferably AWS Strong analytical and problem-solving skills Solid foundation in mathematics and statistics Preferred Qualifications Experience with MLOps tools such as MLflow or Kubeflow. Knowledge of big data technologies like Apache Spark, Hadoop, or Databricks. Experience in NLP, Computer Vision, or other advanced AI domains. Relevant certifications (AWS, Azure, Google Cloud, Coursera, edX).
04/09/2026
Note: Candidates with Green Card, Green Card EAD, H1B, or US Citizen status are NOT eligible for this role. Job Title: Machine Learning Engineer / AI Engineer Location: Reston, VA or Plano, TX Contact email: Job Overview We are seeking a highly skilled Machine Learning Engineer / AI Engineer to design, develop, and deploy scalable machine learning and deep learning solutions. The ideal candidate will have strong programming expertise, hands-on experience with AI frameworks, and the ability to work across the full machine learning lifecycle. Key Responsibilities Develop, train, and optimize machine learning and deep learning models using frameworks such as TensorFlow and PyTorch. Build and maintain end-to-end AI/ML pipelines from data ingestion to model deployment. Analyze complex datasets to generate insights and improve model performance. Design and implement scalable AI solutions to solve business problems. Deploy machine learning models on cloud platforms such as Amazon Web Services (AWS). Work within CI/CD environments to ensure seamless model integration and deployment. Collaborate with cross-functional teams to identify and deliver AI-driven solutions. Required Qualifications Strong programming skills in Python (preferred), R, or Java Hands-on experience with machine learning algorithms and frameworks Experience with cloud platforms, preferably AWS Strong analytical and problem-solving skills Solid foundation in mathematics and statistics Preferred Qualifications Experience with MLOps tools such as MLflow or Kubeflow. Knowledge of big data technologies like Apache Spark, Hadoop, or Databricks. Experience in NLP, Computer Vision, or other advanced AI domains. Relevant certifications (AWS, Azure, Google Cloud, Coursera, edX).
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).
04/09/2026
Full time
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).
04/09/2026
Full time
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).
04/09/2026
Full time
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).
City/State Virginia Beach, VA Work Shift First (Days) Overview: Sentara is hiring for a Senior Data Scientist! This position is fully remote. Overview We are seeking a highly skilled and experienced Data Science ML Operations and Gen AI Engineer (or Senior) to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better healthcare. The Senior Data Scientist will leverage data to improve healthcare outcomes and drive data-driven decision-making. Leveraging expertise in statistical analysis and machine learning, this role will collaborate with cross-functional teams to solve complex healthcare challenges and enhance patient care. This role will directly contribute to advancing medical research, optimizing healthcare processes, and delivering innovative solutions in the healthcare industry. As a Senior ML Engineer on our team, you will play a crucial role in identifying gaps in our existing ML platform and architecting and building solutions to address those gaps. You will also collaborate with the AI team's ML Scientists and our partner data engineering and software development teams to bring ML AND Gen AI models to production and maintain their health and integrity while in production. Your expertise in machine learning and Gen AI, coupled with a strong background in software development, will be instrumental in driving the success of Sentara's AI/ML initiatives. Qualifications: • 5+ years building production software/ML systems, including 1+ years of experience with LLMs/GenAI. • Proficient in Python and one major DL/LLM stack (e.g., PyTorch/Transformers); experience with LangChain/LlamaIndex, vector DBs, and cloud (AWS/Azure/GCP). • Demonstrated delivery of RAG, prompt engineering, evaluation frameworks, and guardrails in production. • Strength in APIs, distributed systems, and ML Ops (K8s, CI/CD, monitoring). • Experience with EPIC health platform is highly preferred • Experience with ML platforms and ML Ops: Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring. • Experience with SQL and big data platforms such as Postgres, Redshift and Snowflake • Experience with Agile/Scrum methodology and best practices Preferred: • Previous work experience with Generative AI and ML Ops in healthcare EPIC environment • Understanding of use and implementation of Vector Databases • Kubernetes container orchestration experience Responsibilities • Responsible for design and development of production-grade Machine Learning ops and Gen AI solutions • Lead hands-on delivery of scalable GenAI solutions from problem framing prototyping evaluation production monitoring. • Build internal copilots/assistants (knowledge search, code/content generation) and client-facing products (conversational analytics, summarization, recommendations, workflow automation). • Design RAG pipelines, embedding strategies, vector search, and model orchestration; evaluate fine-tuning vs. prompt engineering. • Implement guardrails, safety filters, prompt/version management, latency/throughput optimizations, and cost controls. • ML platform and ML Ops: Identify areas that require improvements or additional functionalities and use your expertise in machine learning and software engineering to architect and develop solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to pinpoint opportunities for enhancement. Develop tools and solutions that help the team build, deploy, and monitor AI/ML solutions efficiently. • System scalability and reliability: Optimize the scalability, performance, and reliability and AI Team solutions by implementing best practices and leveraging industry-standard technologies. Collaborate with infrastructure teams to ensure smooth integration and deployment of ML solutions. Design scalable and efficient systems that leverage the power of machine learning for enhanced performance and capabilities. • Data processing and workflow pipelines: Streamline data ingestion, preprocessing, feature engineering, and model training workflows to improve efficiency and reduce latency. Work with data engineering and data platform teams to design and implement robust data pipelines that support the AI team's needs. • Model deployment and monitoring: Evaluate and optimize model prototypes for real-world performance. Work with infrastructure and development teams to integrate ML models into production systems. Work closely with partner teams to communicate and understand technical requirements and challenges. • As part of Sentara's Data Science team you will be responsible for implementation and operationalization of AI/ML models. You will work with other machine learning engineers, data scientists, software engineers and platform engineers to ensure success of the AI/ML implementations. Specific responsibilities will include: • Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc. • Take offline models data scientists build and turn them into a real machine learning production system. Education Bachelor's Degree (Required) Certification/Licensure No specific certification or licensure requirements Experience Required to have 5+ years of experience as a Data Scientist with a strong focus on Azure and Microsoft Data Science, AI, and machine learning toolsets. Required to have strong problem-solving skills and the ability to tackle complex healthcare challenges using data-driven approaches. Can help the Data Science infrastructure building up, working with ML Ops team for model implementation, mentoring and developing junior staff. Required to have s trong proficiency in data analysis, data manipulation, and data visualization using Python. Required to have f amiliarity with healthcare-related datasets, medical terminologies, and electronic health records (EHR) data. Required to have knowledge of statistical techniques, hypothesis testing, and experimental design for healthcare research. Required to have s trong machine learning expertise: Proficient in machine learning algorithms, statistical modeling, and data analysis. Hands-on experience with standard ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, XGBoost, TensorFlow, or Keras). Required to have solid understanding of data engineering principles, data structures, and algorithms. Proficient in Python and/or other programming languages commonly used in ML development. Required to have experience in technologies, frameworks and architecture like Java or Python, Angular, React, JSON, Application Servers, CI/CD is preferred. Required to have experience with one or more AI automations platforms like Kubeflow pipeline, MLFlow, Azure Pipeline, AWS Sage Maker Pipeline, Airflow, Jenkins, Spark, Hadoop, Kafka, Jira and GIT. We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for full-time employment is: $91,416.00 - $152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities. Benefits: Caring For Your Family and Your Career • Medical, Dental, Vision plans • Adoption, Fertility and Surrogacy Reimbursement up to $10,000 • Paid Time Off and Sick Leave • Paid Parental & Family Caregiver Leave • Emergency Backup Care • Long-Term, Short-Term Disability, and Critical Illness plans • Life Insurance • 401k/403B with Employer Match • Tuition Assistance - $5,250/year and discounted educational opportunities through Guild Education • Student Debt Pay Down - $10,000 • Reimbursement for certifications and free access to complete CEUs and professional development •Pet Insurance •Legal Resources Plan •Colleagues have the opportunity to earn an annual discretionary bonus ifestablished system and employee eligibility criteria is met. Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves. In support of our mission "to improve health every day," this is a tobacco-free environment. For positions that are available as remote work, Sentara Health employs associates in the following states: Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.
04/08/2026
Full time
City/State Virginia Beach, VA Work Shift First (Days) Overview: Sentara is hiring for a Senior Data Scientist! This position is fully remote. Overview We are seeking a highly skilled and experienced Data Science ML Operations and Gen AI Engineer (or Senior) to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better healthcare. The Senior Data Scientist will leverage data to improve healthcare outcomes and drive data-driven decision-making. Leveraging expertise in statistical analysis and machine learning, this role will collaborate with cross-functional teams to solve complex healthcare challenges and enhance patient care. This role will directly contribute to advancing medical research, optimizing healthcare processes, and delivering innovative solutions in the healthcare industry. As a Senior ML Engineer on our team, you will play a crucial role in identifying gaps in our existing ML platform and architecting and building solutions to address those gaps. You will also collaborate with the AI team's ML Scientists and our partner data engineering and software development teams to bring ML AND Gen AI models to production and maintain their health and integrity while in production. Your expertise in machine learning and Gen AI, coupled with a strong background in software development, will be instrumental in driving the success of Sentara's AI/ML initiatives. Qualifications: • 5+ years building production software/ML systems, including 1+ years of experience with LLMs/GenAI. • Proficient in Python and one major DL/LLM stack (e.g., PyTorch/Transformers); experience with LangChain/LlamaIndex, vector DBs, and cloud (AWS/Azure/GCP). • Demonstrated delivery of RAG, prompt engineering, evaluation frameworks, and guardrails in production. • Strength in APIs, distributed systems, and ML Ops (K8s, CI/CD, monitoring). • Experience with EPIC health platform is highly preferred • Experience with ML platforms and ML Ops: Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring. • Experience with SQL and big data platforms such as Postgres, Redshift and Snowflake • Experience with Agile/Scrum methodology and best practices Preferred: • Previous work experience with Generative AI and ML Ops in healthcare EPIC environment • Understanding of use and implementation of Vector Databases • Kubernetes container orchestration experience Responsibilities • Responsible for design and development of production-grade Machine Learning ops and Gen AI solutions • Lead hands-on delivery of scalable GenAI solutions from problem framing prototyping evaluation production monitoring. • Build internal copilots/assistants (knowledge search, code/content generation) and client-facing products (conversational analytics, summarization, recommendations, workflow automation). • Design RAG pipelines, embedding strategies, vector search, and model orchestration; evaluate fine-tuning vs. prompt engineering. • Implement guardrails, safety filters, prompt/version management, latency/throughput optimizations, and cost controls. • ML platform and ML Ops: Identify areas that require improvements or additional functionalities and use your expertise in machine learning and software engineering to architect and develop solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to pinpoint opportunities for enhancement. Develop tools and solutions that help the team build, deploy, and monitor AI/ML solutions efficiently. • System scalability and reliability: Optimize the scalability, performance, and reliability and AI Team solutions by implementing best practices and leveraging industry-standard technologies. Collaborate with infrastructure teams to ensure smooth integration and deployment of ML solutions. Design scalable and efficient systems that leverage the power of machine learning for enhanced performance and capabilities. • Data processing and workflow pipelines: Streamline data ingestion, preprocessing, feature engineering, and model training workflows to improve efficiency and reduce latency. Work with data engineering and data platform teams to design and implement robust data pipelines that support the AI team's needs. • Model deployment and monitoring: Evaluate and optimize model prototypes for real-world performance. Work with infrastructure and development teams to integrate ML models into production systems. Work closely with partner teams to communicate and understand technical requirements and challenges. • As part of Sentara's Data Science team you will be responsible for implementation and operationalization of AI/ML models. You will work with other machine learning engineers, data scientists, software engineers and platform engineers to ensure success of the AI/ML implementations. Specific responsibilities will include: • Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc. • Take offline models data scientists build and turn them into a real machine learning production system. Education Bachelor's Degree (Required) Certification/Licensure No specific certification or licensure requirements Experience Required to have 5+ years of experience as a Data Scientist with a strong focus on Azure and Microsoft Data Science, AI, and machine learning toolsets. Required to have strong problem-solving skills and the ability to tackle complex healthcare challenges using data-driven approaches. Can help the Data Science infrastructure building up, working with ML Ops team for model implementation, mentoring and developing junior staff. Required to have s trong proficiency in data analysis, data manipulation, and data visualization using Python. Required to have f amiliarity with healthcare-related datasets, medical terminologies, and electronic health records (EHR) data. Required to have knowledge of statistical techniques, hypothesis testing, and experimental design for healthcare research. Required to have s trong machine learning expertise: Proficient in machine learning algorithms, statistical modeling, and data analysis. Hands-on experience with standard ML frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, XGBoost, TensorFlow, or Keras). Required to have solid understanding of data engineering principles, data structures, and algorithms. Proficient in Python and/or other programming languages commonly used in ML development. Required to have experience in technologies, frameworks and architecture like Java or Python, Angular, React, JSON, Application Servers, CI/CD is preferred. Required to have experience with one or more AI automations platforms like Kubeflow pipeline, MLFlow, Azure Pipeline, AWS Sage Maker Pipeline, Airflow, Jenkins, Spark, Hadoop, Kafka, Jira and GIT. We provide market-competitive compensation packages, inclusive of base pay, incentives, and benefits. The base pay rate for full-time employment is: $91,416.00 - $152,380.80. Additional compensation may be available for this role such as shift differentials, standby/on-call, overtime, premiums, extra shift incentives, or bonus opportunities. Benefits: Caring For Your Family and Your Career • Medical, Dental, Vision plans • Adoption, Fertility and Surrogacy Reimbursement up to $10,000 • Paid Time Off and Sick Leave • Paid Parental & Family Caregiver Leave • Emergency Backup Care • Long-Term, Short-Term Disability, and Critical Illness plans • Life Insurance • 401k/403B with Employer Match • Tuition Assistance - $5,250/year and discounted educational opportunities through Guild Education • Student Debt Pay Down - $10,000 • Reimbursement for certifications and free access to complete CEUs and professional development •Pet Insurance •Legal Resources Plan •Colleagues have the opportunity to earn an annual discretionary bonus ifestablished system and employee eligibility criteria is met. Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30,000-member workforce. Diversity, inclusion, and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves. In support of our mission "to improve health every day," this is a tobacco-free environment. For positions that are available as remote work, Sentara Health employs associates in the following states: Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, and Wyoming.
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
04/08/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
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).
04/08/2026
Full time
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).
04/08/2026
Full time
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).
04/08/2026
Full time
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).
Sr. Distinguished Machine Learning Engineer (Remote-Eligible) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. 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 how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment. What you'll do in the role: Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation. 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. 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. Capital One is open to hiring a Remote Employee for this opportunity 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: 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company. 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. Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer McLean, VA: $314,800 - $359,300 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).
04/08/2026
Full time
Sr. Distinguished Machine Learning Engineer (Remote-Eligible) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. 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 how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment. What you'll do in the role: Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation. 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. 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. Capital One is open to hiring a Remote Employee for this opportunity 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: 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company. 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. Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer McLean, VA: $314,800 - $359,300 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).
Sr. Distinguished Machine Learning Engineer (Remote-Eligible) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. 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 how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment. What you'll do in the role: Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation. 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. 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. Capital One is open to hiring a Remote Employee for this opportunity 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: 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company. 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. Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer McLean, VA: $314,800 - $359,300 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 . click apply for full job details
04/07/2026
Full time
Sr. Distinguished Machine Learning Engineer (Remote-Eligible) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. 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 how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment. What you'll do in the role: Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation. 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. 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. Capital One is open to hiring a Remote Employee for this opportunity 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: 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company. 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. Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer McLean, VA: $314,800 - $359,300 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 . click apply for full job details
Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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).
04/07/2026
Full time
Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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 (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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).
04/07/2026
Full time
Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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 (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and componentsthat solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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).
04/06/2026
Full time
Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and componentsthat solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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 (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and componentsthat solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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).
04/06/2026
Full time
Lead Machine Learning Engineer (Gen AI, Python, Go, AWS) As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving team, you'll be part of an Agile team dedicated to designing, building, and productionizing Generative AI applications and Agentic Workflow systems at massive scale. You'll participate in the detailed technical design, development, and implementation of complex machine learning applications leveraging cloud-native platforms. You'll focus on building robust ML serving architecture, developing high-performance application code, and ensuring the high availability, security, and low latency of our Generative AI solutions. You will collaborate closely with multiple other AI/ML teams to drive innovation and continuously 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 deliver GenAI models and componentsthat solve complex business problems, while working in collaboration with the Product and Data Science teams. Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models. Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment. Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications. Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints. Retrain, maintain, and monitor models in production. Construct optimized, scalable 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. Use programming languages like Python, Go, 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, Go 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 San Francisco, 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).
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
04/05/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
Job Title: MLOps Engineer Job Location: Houston, TX 77002 (Hybrid - 4 Days a week in office) Job Contract: 8 Months+ contract (with possible extension) Note: W2 only Job Description: Must-have: Hands-on experience with AWS, Microsoft Azure, and Snowflake in building or supporting production ML/data platforms. Job Summary: We are seeking an MLOps Engineer to design, deploy, monitor, and maintain machine learning solutions in production across AWS, Microsoft Azure, and Snowflake environments. This role will partner with data scientists and cloud teams to operationalize ML models, automate pipelines, and build reliable, secure, and scalable ML platforms. The ideal candidate has strong experience in the end-to-end ML lifecycle, cloud-native deployment, CI/CD automation, model monitoring, and production data pipelines, with hands-on expertise in AWS, Azure, and Snowflake. Key Responsibilities: Design and implement end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring. Deploy and manage ML models in production across AWS, Azure, and Snowflake-based ecosystems. Build batch and real-time inference pipelines using cloud-native and platform-native services Automate model packaging, testing, release, and rollback using CI/CD best practices. Integrate ML workflows with services such as AWS SageMaker, AWS Lambda, Azure Machine Learning, Azure Data Factory, and Snowflake. Build and maintain orchestration workflows using tools such as Airflow, Azure Data Factory, or similar platforms. Implement experiment tracking, model registry, and model governance processes. Monitor model accuracy, drift, latency, throughput, pipeline failures, and infrastructure usage. Establish deployment strategies such as canary, shadow, blue-green, and rollback mechanisms. Collaborate with cross-functional teams to move models from research to production. Ensure security, compliance, traceability, and access control for models and data across cloud environments. Optimize platform performance, reliability, and cost across AWS, Azure, and Snowflake. Document architecture, deployment standards, and operational procedures. Required Qualifications: Master's or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar Five or more years of relevant experiences Proven experience in MLOps, ML engineering, platform engineering, or DevOps Strong hands-on experience with AWS, Microsoft Azure, and Snowflake Strong programming skills in Python and SQL Experience deploying and managing ML models in production Experience with cloud ML services such as AWS SageMaker and Azure Machine Learning Experience building data pipelines and integrating with Snowflake Knowledge of CI/CD pipelines, infrastructure automation, and model versioning Experience with containerization and orchestration tools such as Docker and Kubernetes Experience with workflow orchestration tools such as Airflow, Azure Data Factory, or similar Familiarity with model monitoring, logging, alerting, and observability Solid understanding of data engineering concepts, APIs, and distributed processing Strong troubleshooting, communication, and cross-team collaboration skills Preferred Qualifications: Experience with Snowflake Cortex AI, Snowpark, or ML workloads in Snowflake Experience with AWS Bedrock, Azure Open AI, or production LLM workflows Experience with real-time inference, event-driven pipelines, and server less architectures Familiarity with feature stores, vector databases, and RAG-based systems Experience with Terraform, Cloud Formation, or Azure infrastructure-as-code tools Understanding of security, compliance, and governance requirements for regulated environments Experience with production A/B testing, shadow deployment, and rollback strategies
04/01/2026
Full time
Job Title: MLOps Engineer Job Location: Houston, TX 77002 (Hybrid - 4 Days a week in office) Job Contract: 8 Months+ contract (with possible extension) Note: W2 only Job Description: Must-have: Hands-on experience with AWS, Microsoft Azure, and Snowflake in building or supporting production ML/data platforms. Job Summary: We are seeking an MLOps Engineer to design, deploy, monitor, and maintain machine learning solutions in production across AWS, Microsoft Azure, and Snowflake environments. This role will partner with data scientists and cloud teams to operationalize ML models, automate pipelines, and build reliable, secure, and scalable ML platforms. The ideal candidate has strong experience in the end-to-end ML lifecycle, cloud-native deployment, CI/CD automation, model monitoring, and production data pipelines, with hands-on expertise in AWS, Azure, and Snowflake. Key Responsibilities: Design and implement end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, deployment, and monitoring. Deploy and manage ML models in production across AWS, Azure, and Snowflake-based ecosystems. Build batch and real-time inference pipelines using cloud-native and platform-native services Automate model packaging, testing, release, and rollback using CI/CD best practices. Integrate ML workflows with services such as AWS SageMaker, AWS Lambda, Azure Machine Learning, Azure Data Factory, and Snowflake. Build and maintain orchestration workflows using tools such as Airflow, Azure Data Factory, or similar platforms. Implement experiment tracking, model registry, and model governance processes. Monitor model accuracy, drift, latency, throughput, pipeline failures, and infrastructure usage. Establish deployment strategies such as canary, shadow, blue-green, and rollback mechanisms. Collaborate with cross-functional teams to move models from research to production. Ensure security, compliance, traceability, and access control for models and data across cloud environments. Optimize platform performance, reliability, and cost across AWS, Azure, and Snowflake. Document architecture, deployment standards, and operational procedures. Required Qualifications: Master's or Advanced degree (PhD) in Computer Science, Computer Engineering, or Similar Five or more years of relevant experiences Proven experience in MLOps, ML engineering, platform engineering, or DevOps Strong hands-on experience with AWS, Microsoft Azure, and Snowflake Strong programming skills in Python and SQL Experience deploying and managing ML models in production Experience with cloud ML services such as AWS SageMaker and Azure Machine Learning Experience building data pipelines and integrating with Snowflake Knowledge of CI/CD pipelines, infrastructure automation, and model versioning Experience with containerization and orchestration tools such as Docker and Kubernetes Experience with workflow orchestration tools such as Airflow, Azure Data Factory, or similar Familiarity with model monitoring, logging, alerting, and observability Solid understanding of data engineering concepts, APIs, and distributed processing Strong troubleshooting, communication, and cross-team collaboration skills Preferred Qualifications: Experience with Snowflake Cortex AI, Snowpark, or ML workloads in Snowflake Experience with AWS Bedrock, Azure Open AI, or production LLM workflows Experience with real-time inference, event-driven pipelines, and server less architectures Familiarity with feature stores, vector databases, and RAG-based systems Experience with Terraform, Cloud Formation, or Azure infrastructure-as-code tools Understanding of security, compliance, and governance requirements for regulated environments Experience with production A/B testing, shadow deployment, and rollback strategies
KMM - an ISO 9001:2015, CMMI Level 2 certified company - provides high-quality IT consulting services and innovative solutions by using the most effective and modern technologies. We have a core group of Subject Matter Experts with certifications and immense experience in successfully delivering mission-critical solutions. We have extensive industry experience in the financial, insurance, Health IT, media, marketing, retail, and government markets. We have a proven track record in understanding client's business challenges, determine a customer-focused solution, and provide the technical implementation and documentation to bring it to fruition. Position: AI Engineer Location: Reston, VA (3 days a week onsite) Description: Develop and optimize machine learning and deep learning models using frameworks like TensorFlow or PyTorch. Strong skills in programming languages such as Python, R, or Java, essential for developing AI applications. Build and maintain AI pipelines, from data processing to model deployment. Analyze complex datasets to uncover insights and improve model performance. Ability to analyze complex problems and develop effective AI-driven solutions. Deploy AI solutions on cloud platforms such as AWS. Collaborate with technical and business teams to identify AI opportunities and deliver impactful solutions. Deep understanding of deploying AI applications within a CICD environment, specifically AWS. Preferred qualifications: Experience with MLOps tools (e.g., MLflow, Kubeflow). Knowledge of big data technologies (Spark, Hadoop, Databricks). Background in NLP, computer vision, or other advanced AI techniques. Relevant certifications (Coursera, edX, AWS, Azure, Google Cloud). Required qualifications: Strong programming skills in Python, R, or Java. Hands-on experience with machine learning algorithms and frameworks. Familiarity with major cloud platforms for AI deployment. Strong analytical and problem solving skills. Solid foundation in mathematics and statistics. AI Engineer
04/01/2026
Full time
KMM - an ISO 9001:2015, CMMI Level 2 certified company - provides high-quality IT consulting services and innovative solutions by using the most effective and modern technologies. We have a core group of Subject Matter Experts with certifications and immense experience in successfully delivering mission-critical solutions. We have extensive industry experience in the financial, insurance, Health IT, media, marketing, retail, and government markets. We have a proven track record in understanding client's business challenges, determine a customer-focused solution, and provide the technical implementation and documentation to bring it to fruition. Position: AI Engineer Location: Reston, VA (3 days a week onsite) Description: Develop and optimize machine learning and deep learning models using frameworks like TensorFlow or PyTorch. Strong skills in programming languages such as Python, R, or Java, essential for developing AI applications. Build and maintain AI pipelines, from data processing to model deployment. Analyze complex datasets to uncover insights and improve model performance. Ability to analyze complex problems and develop effective AI-driven solutions. Deploy AI solutions on cloud platforms such as AWS. Collaborate with technical and business teams to identify AI opportunities and deliver impactful solutions. Deep understanding of deploying AI applications within a CICD environment, specifically AWS. Preferred qualifications: Experience with MLOps tools (e.g., MLflow, Kubeflow). Knowledge of big data technologies (Spark, Hadoop, Databricks). Background in NLP, computer vision, or other advanced AI techniques. Relevant certifications (Coursera, edX, AWS, Azure, Google Cloud). Required qualifications: Strong programming skills in Python, R, or Java. Hands-on experience with machine learning algorithms and frameworks. Familiarity with major cloud platforms for AI deployment. Strong analytical and problem solving skills. Solid foundation in mathematics and statistics. AI Engineer
Staff Data Scientist Location: Beachwood, OH Shift: Monday - Friday 8am - 5pm (Onsite 4 days a week) (Possible remote for the right candidate) Position Summary: The Staff Data Scientist will be a key role in the Data Science and Analytics team tasked with providing technical leadership for the establishment of enterprise wide capabilities in data science, AI and predictive analytics. The Staff Data Scientist will typically work on 3-5 large projects concurrently that have organization-wide impact. In addition to these projects, the Staff Data Scientist will provide technical consultation, advice and training on all major on-going Data Science and Analytics projects. When required, the Staff Data Scientist will also act as a project manager where vendors, suppliers and consultants are engaged on key strategic and emerging technology initiatives. Major Responsibilities: Identifying High Value Analytics & AI Opportunities Partner with business leaders to identify opportunities where predictive analytics, machine learning, or generative AI can improve productivity, reduce cost, or unlock new capabilities. Develop clear business cases and ROI models to prioritize initiatives and communicate value to senior leadership. Lead Data Science Projects Translate complex business requirements into robust, scalable technical solutions. Select and implement appropriate modeling techniques, including classical ML, deep learning, generative AI, and reinforcement learning where applicable. Oversee the full model lifecycle: data exploration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement. Ensure solutions are production ready, maintainable, and aligned with MLOps best practices. Drive organization wide adoption of models and AI systems through clear communication, documentation, and stakeholder engagement. Technical Guidance & Thought Leadership Provide expert consultation on ML algorithms, model tuning, experimentation frameworks, and cloud native data engineering patterns. Mentor data scientists, ML engineers and AI engineers; support skill development in areas such as forecasting, ML modeling, generative AI, vector databases, and modern ETL/ELT workflows. Contribute to the development of internal standards, reusable components, and best practice guidelines. Project Management Develop and maintain project plans, milestones, and communication strategies for strategic initiatives. Facilitate regular updates with stakeholders, executives, and cross functional partners. Coordinate with vendors, consultants, and technology partners when external expertise is required Lead technology change in Data Science, Analytics and AI Evaluate emerging technologies including generative AI platforms, MLOps tools, cloud services, and data engineering frameworks to determine applicability and business value. Recommend and influence adoption of modern, flexible, and scalable technologies that support a unified enterprise data and AI platform. Drive experimentation and prototyping to accelerate innovation and reduce time to value. Qualifications: Master's Degree required; preferred concentrations in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field. PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field. 7+ years of experience along with a PhD in a related field OR 10+ years of experience along with a Master's degree in a related field required. Advanced experience developing and deploying machine learning models using Python and modern ML frameworks (e.g., Scikitlearn, PyTorch, TensorFlow). Strong applied expertise across core ML techniques, including regression, tree based models, clustering, deep learning, and NLP. Familiarity with generative AI and LLMs, including prompt engineering, finetuning, embeddings, and vector databases. Solid understanding of MLOps practices, including CI/CD for ML, automated training pipelines, model versioning, monitoring, and model governance. Hands on experience with cloud based ML platforms (AWS, Azure, or GCP) and containerization/orchestration tools such as Docker and Kubernetes. Working knowledge of modern data ecosystems (Snowflake, Redshift) and the ability to collaborate effectively with data engineering teams when needed. Advanced skill in statistical modeling, SQL, and database concepts required. Demonstrated experience leading small technical teams or pods, providing mentorship and technical direction. Familiarity with Logistics industry is preferred. Regular, predictable, full attendance is an essential function of the job Willingness to travel as necessary, work the required schedule, work at the specific location required, complete Penske employment application, submit to a background investigation (to include past employment, education, and criminal history) and drug screening are required. Physical Requirements: -The physical and mental demands described here are representative of those that must be met by an associate to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. -The associate will be required to: read; communicate verbally and/or in written form; remember and analyze certain information; and remember and understand certain instructions or guidelines. -While performing the duties of this job, the associate may be required to stand, walk, and sit. The associate is frequently required to use hands to touch, handle, and feel, and to reach with hands and arms. The associate must be able to occasionally lift and/or move up to 25lbs/12kg. -Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception and the ability to adjust focus. Penske is an Equal Opportunity Employer. About Penske Logistics Penske Logistics engineers state-of-the-art transportation, warehousing and freight management solutions that deliver powerful business results for market-leading companies. With operations in North America, South America, Europe and Asia, Penske and its associates help businesses move forward by increasing visibility and driving down supply-chain costs. Visit Penske Logistics to learn more. Job Category: Information Technology Job Family: Analytics & Intelligence Address: 3000 Auburn Dr Primary Location: US-OH-Beachwood Employer: Penske Logistics LLC Req ID:
04/01/2026
Full time
Staff Data Scientist Location: Beachwood, OH Shift: Monday - Friday 8am - 5pm (Onsite 4 days a week) (Possible remote for the right candidate) Position Summary: The Staff Data Scientist will be a key role in the Data Science and Analytics team tasked with providing technical leadership for the establishment of enterprise wide capabilities in data science, AI and predictive analytics. The Staff Data Scientist will typically work on 3-5 large projects concurrently that have organization-wide impact. In addition to these projects, the Staff Data Scientist will provide technical consultation, advice and training on all major on-going Data Science and Analytics projects. When required, the Staff Data Scientist will also act as a project manager where vendors, suppliers and consultants are engaged on key strategic and emerging technology initiatives. Major Responsibilities: Identifying High Value Analytics & AI Opportunities Partner with business leaders to identify opportunities where predictive analytics, machine learning, or generative AI can improve productivity, reduce cost, or unlock new capabilities. Develop clear business cases and ROI models to prioritize initiatives and communicate value to senior leadership. Lead Data Science Projects Translate complex business requirements into robust, scalable technical solutions. Select and implement appropriate modeling techniques, including classical ML, deep learning, generative AI, and reinforcement learning where applicable. Oversee the full model lifecycle: data exploration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement. Ensure solutions are production ready, maintainable, and aligned with MLOps best practices. Drive organization wide adoption of models and AI systems through clear communication, documentation, and stakeholder engagement. Technical Guidance & Thought Leadership Provide expert consultation on ML algorithms, model tuning, experimentation frameworks, and cloud native data engineering patterns. Mentor data scientists, ML engineers and AI engineers; support skill development in areas such as forecasting, ML modeling, generative AI, vector databases, and modern ETL/ELT workflows. Contribute to the development of internal standards, reusable components, and best practice guidelines. Project Management Develop and maintain project plans, milestones, and communication strategies for strategic initiatives. Facilitate regular updates with stakeholders, executives, and cross functional partners. Coordinate with vendors, consultants, and technology partners when external expertise is required Lead technology change in Data Science, Analytics and AI Evaluate emerging technologies including generative AI platforms, MLOps tools, cloud services, and data engineering frameworks to determine applicability and business value. Recommend and influence adoption of modern, flexible, and scalable technologies that support a unified enterprise data and AI platform. Drive experimentation and prototyping to accelerate innovation and reduce time to value. Qualifications: Master's Degree required; preferred concentrations in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field. PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data Science or related quantitative field. 7+ years of experience along with a PhD in a related field OR 10+ years of experience along with a Master's degree in a related field required. Advanced experience developing and deploying machine learning models using Python and modern ML frameworks (e.g., Scikitlearn, PyTorch, TensorFlow). Strong applied expertise across core ML techniques, including regression, tree based models, clustering, deep learning, and NLP. Familiarity with generative AI and LLMs, including prompt engineering, finetuning, embeddings, and vector databases. Solid understanding of MLOps practices, including CI/CD for ML, automated training pipelines, model versioning, monitoring, and model governance. Hands on experience with cloud based ML platforms (AWS, Azure, or GCP) and containerization/orchestration tools such as Docker and Kubernetes. Working knowledge of modern data ecosystems (Snowflake, Redshift) and the ability to collaborate effectively with data engineering teams when needed. Advanced skill in statistical modeling, SQL, and database concepts required. Demonstrated experience leading small technical teams or pods, providing mentorship and technical direction. Familiarity with Logistics industry is preferred. Regular, predictable, full attendance is an essential function of the job Willingness to travel as necessary, work the required schedule, work at the specific location required, complete Penske employment application, submit to a background investigation (to include past employment, education, and criminal history) and drug screening are required. Physical Requirements: -The physical and mental demands described here are representative of those that must be met by an associate to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. -The associate will be required to: read; communicate verbally and/or in written form; remember and analyze certain information; and remember and understand certain instructions or guidelines. -While performing the duties of this job, the associate may be required to stand, walk, and sit. The associate is frequently required to use hands to touch, handle, and feel, and to reach with hands and arms. The associate must be able to occasionally lift and/or move up to 25lbs/12kg. -Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception and the ability to adjust focus. Penske is an Equal Opportunity Employer. About Penske Logistics Penske Logistics engineers state-of-the-art transportation, warehousing and freight management solutions that deliver powerful business results for market-leading companies. With operations in North America, South America, Europe and Asia, Penske and its associates help businesses move forward by increasing visibility and driving down supply-chain costs. Visit Penske Logistics to learn more. Job Category: Information Technology Job Family: Analytics & Intelligence Address: 3000 Auburn Dr Primary Location: US-OH-Beachwood Employer: Penske Logistics LLC Req ID:
Department: Sch of Nursing - 440100 Career Area : Information Technology Posting Open Date: 10/23/2025 Application Deadline: 01/11/2026 Position Type: Temporary Staff (SHRA) Position Title : AI/LLM Developer/Engineer Position Number: Vacancy ID: S026301 Full-time/Part-time: Full-Time Temporary Hours per week: 40 Position Location: North Carolina, US Hiring Range: $26.04 - $33.85 per hour Estimated Duration of Appointment: 6 months not to exceed 11 months Be a Tar Heel!: A global higher education leader in innovative teaching, research and public service, the University of North Carolina at Chapel Hill consistently ranks as one of the nation's top public universities . Known for its beautiful campus, world-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn.One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span the campus and community.University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates for performing arts events. Primary Purpose of Organizational Unit: The mission of the School of Nursing is to enhance and improve the health and well-being of the people of North Carolina and the nation, and, as relevant and appropriate, the people of other nations, through its programs of education, research, and scholarship, and through clinical practice and community service. The University of North Carolina at Chapel Hill School of Nursing has been the leader in nursing education in North Carolina throughout its history. Established in 1950, it was the first school of nursing in North Carolina to offer a four-year baccalaureate nursing degree program followed by the first master's degree program, the first continuing education program for nurses, first doctoral program, and first accelerated BSN option to students with college degrees. Today, the School is renowned for its academic programs, its research and its commitment to clinical and community service within state, national and global communities. Position Summary: The Center for Virtual Care Value and Excellence (ViVE), led by Dr. Saif Khairat, is seeking an AI/LLM Developer/Engineer to join our AI research team. This is an exciting opportunity to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with a collaborative spirit and a drive for problem-solving. You'll join a multidisciplinary team that values diversity and brings together expertise in software engineering, big data, clinical informatics, and medicine. Key Responsibilities Design, fine-tune, and evaluate large language models (LLMs) tailored to domain-specific applications using techniques such as transfer learning, LoRA, and reinforcement learning with human feedback (RLHF). Build intelligent applications powered by LLMs, including chatbots, virtual agents, clinical decision tools, or document analyzers, using frameworks like LangChain, LlamaIndex, or semantic search pipelines. Develop scalable LLM pipelines and infrastructure, including data ingestion, preprocessing, model serving (via GPU/TPU), and continuous performance monitoring. Integrate commercial and open-source LLMs (e.g., OpenAI GPT, Claude, Mistral, LLaMA) via APIs or local deployment into digital health or enterprise systems. Craft and iterate prompts using advanced prompt engineering and chain-of-thought strategies to improve output relevance, tone, factuality, and task completion. Implement retrieval-augmented generation (RAG) architectures to enhance context awareness using vector databases (e.g., Pinecone, FAISS, Weaviate). Evaluate LLM performance using automated and human-in-the-loop methods to assess accuracy, hallucination, safety, and user satisfaction. Collaborate across disciplines with data scientists, UX designers, domain experts, and MLOps to ensure usability, performance, and alignment with real-world needs. Monitor and optimize system performance, including latency, throughput, token usage, and model cost-effectiveness across deployment environments. Stay current with advancements in generative AI, contributing to the internal knowledge base and driving adoption of best practices for ethical and responsible LLM use. Minimum Education and Experience Requirements: Bachelor's degree in Computer Science, Computer Information Systems, Computer Engineering, or closely related degree from an appropriately accredited institution and three years of experience in operations analysis and design, systems programming, or closely related area; or a - Bachelor's degree from an appropriately accredited institution and four years of experience in operations analysis and design, systems programming or closely related area; or an Associate's degree in Computer Information Technology, Computer Engineering Technology, or Networking Technology from an appropriately accredited institution and five years of experience in operations analysis and design, systems programming, or closely related area; or an equivalent combination of education and experience. - Journey level requires an additional one year of education or experience. - Advanced level requires an additional two years of education or experience. Required Qualifications, Competencies, and Experience: Bachelor's degree in Computer Science, Electrical Engineering, or related fields. Expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), deep learning frameworks. Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or LangChain Familiarity with clinical or healthcare data (e.g., EHRs, clinical notes, structured claims data) Proven research record with peer-reviewed publications in relevant fields Strong problem-solving skills and the ability to work in a collaborative environment. Preferred Qualifications, Competencies, and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG) Experience deploying large-scale machine learning models in cloud environments. Campus Security Authority Responsibilities: Not Applicable.
01/14/2026
Full time
Department: Sch of Nursing - 440100 Career Area : Information Technology Posting Open Date: 10/23/2025 Application Deadline: 01/11/2026 Position Type: Temporary Staff (SHRA) Position Title : AI/LLM Developer/Engineer Position Number: Vacancy ID: S026301 Full-time/Part-time: Full-Time Temporary Hours per week: 40 Position Location: North Carolina, US Hiring Range: $26.04 - $33.85 per hour Estimated Duration of Appointment: 6 months not to exceed 11 months Be a Tar Heel!: A global higher education leader in innovative teaching, research and public service, the University of North Carolina at Chapel Hill consistently ranks as one of the nation's top public universities . Known for its beautiful campus, world-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn.One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span the campus and community.University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates for performing arts events. Primary Purpose of Organizational Unit: The mission of the School of Nursing is to enhance and improve the health and well-being of the people of North Carolina and the nation, and, as relevant and appropriate, the people of other nations, through its programs of education, research, and scholarship, and through clinical practice and community service. The University of North Carolina at Chapel Hill School of Nursing has been the leader in nursing education in North Carolina throughout its history. Established in 1950, it was the first school of nursing in North Carolina to offer a four-year baccalaureate nursing degree program followed by the first master's degree program, the first continuing education program for nurses, first doctoral program, and first accelerated BSN option to students with college degrees. Today, the School is renowned for its academic programs, its research and its commitment to clinical and community service within state, national and global communities. Position Summary: The Center for Virtual Care Value and Excellence (ViVE), led by Dr. Saif Khairat, is seeking an AI/LLM Developer/Engineer to join our AI research team. This is an exciting opportunity to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with a collaborative spirit and a drive for problem-solving. You'll join a multidisciplinary team that values diversity and brings together expertise in software engineering, big data, clinical informatics, and medicine. Key Responsibilities Design, fine-tune, and evaluate large language models (LLMs) tailored to domain-specific applications using techniques such as transfer learning, LoRA, and reinforcement learning with human feedback (RLHF). Build intelligent applications powered by LLMs, including chatbots, virtual agents, clinical decision tools, or document analyzers, using frameworks like LangChain, LlamaIndex, or semantic search pipelines. Develop scalable LLM pipelines and infrastructure, including data ingestion, preprocessing, model serving (via GPU/TPU), and continuous performance monitoring. Integrate commercial and open-source LLMs (e.g., OpenAI GPT, Claude, Mistral, LLaMA) via APIs or local deployment into digital health or enterprise systems. Craft and iterate prompts using advanced prompt engineering and chain-of-thought strategies to improve output relevance, tone, factuality, and task completion. Implement retrieval-augmented generation (RAG) architectures to enhance context awareness using vector databases (e.g., Pinecone, FAISS, Weaviate). Evaluate LLM performance using automated and human-in-the-loop methods to assess accuracy, hallucination, safety, and user satisfaction. Collaborate across disciplines with data scientists, UX designers, domain experts, and MLOps to ensure usability, performance, and alignment with real-world needs. Monitor and optimize system performance, including latency, throughput, token usage, and model cost-effectiveness across deployment environments. Stay current with advancements in generative AI, contributing to the internal knowledge base and driving adoption of best practices for ethical and responsible LLM use. Minimum Education and Experience Requirements: Bachelor's degree in Computer Science, Computer Information Systems, Computer Engineering, or closely related degree from an appropriately accredited institution and three years of experience in operations analysis and design, systems programming, or closely related area; or a - Bachelor's degree from an appropriately accredited institution and four years of experience in operations analysis and design, systems programming or closely related area; or an Associate's degree in Computer Information Technology, Computer Engineering Technology, or Networking Technology from an appropriately accredited institution and five years of experience in operations analysis and design, systems programming, or closely related area; or an equivalent combination of education and experience. - Journey level requires an additional one year of education or experience. - Advanced level requires an additional two years of education or experience. Required Qualifications, Competencies, and Experience: Bachelor's degree in Computer Science, Electrical Engineering, or related fields. Expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), deep learning frameworks. Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or LangChain Familiarity with clinical or healthcare data (e.g., EHRs, clinical notes, structured claims data) Proven research record with peer-reviewed publications in relevant fields Strong problem-solving skills and the ability to work in a collaborative environment. Preferred Qualifications, Competencies, and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG) Experience deploying large-scale machine learning models in cloud environments. Campus Security Authority Responsibilities: Not Applicable.
