Capital One
Mc Lean, Virginia
Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value 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 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 Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 3 years of experience building, scaling, and optimizing ML systems At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: Master's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a similar field 6+ years of experience designing, developing, delivering, and supporting AI services at scale 3+ years of experience developing AI and ML algorithms or technologies using Python 2+ years of experience with Retrieval Augmented Generation (RAG) Experience staying abreast of latest ML research with an intuitive ability to understand scientific publications and judiciously apply novel techniques in production Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences) As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value 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 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 Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 3 years of experience building, scaling, and optimizing ML systems At least 2 years of experience leading teams developing ML solutions Preferred Qualifications: Master's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a similar field 6+ years of experience designing, developing, delivering, and supporting AI services at scale 3+ years of experience developing AI and ML algorithms or technologies using Python 2+ years of experience with Retrieval Augmented Generation (RAG) Experience staying abreast of latest ML research with an intuitive ability to understand scientific publications and judiciously apply novel techniques in production Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer Richmond, VA: $209,000 - $238,500 for Sr. Lead Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Bosch Group
Sunnyvale, California
Company Description "Invented for Life" drives us at Bosch and our vision of future mobility. Autonomous vehicles will change the way we move and at Bosch we are working on making this future a reality. We are now growing our team to solve some of the hardest automated driving problems, and are looking for experts for product engineering of AI-based Autonomous Driving systems. Job Description As the Senior Principal Engineer, E2E AI Training Framework for Autonomous Driving Systems, you will spearhead the development and optimization of the machinery that efficiently trains, optimizes, tests, and releases various E2E AI-based products (e.g. parking, driving, interior sensing) for different trim levels (entry, mid and high) within our L2+ ADAS stack. You will be responsible for defining and driving the technological roadmap for the AI machinery, ensuring its scalability, reliability, and performance. The ideal candidate must have prior industry experience in releasing AI-based L2+ systems, with a proven track record of translating innovative research into production-grade solutions. Key Responsibilities Define and drive execution of the technical roadmap and strategy for the E2E AI machinery, including training pipelines, optimization techniques, simulation and MLOps tooling. Oversee the design, development, and testing of the E2E AI machinery and its interaction with data sources, model repositories, and development targets. Collaborate closely with other functional tech leads (e.g. data engineering, infrastructure) to define and drive the overall architecture of the AI machinery ecosystem. Guide the set-up of a development framework that enables fast evaluation and integration of emerging E2E AI solutions. Guide the transition from research prototypes to production-ready solutions, ensuring performance optimization on automotive-grade hardware and scalability. Leverage your prior industry experience in launching AI-based L2+ systems to implement best practices in system validation, testing (SIL/HIL), and continuous improvement. Mentor and lead a high-caliber team of AI scientists and engineers, fostering a culture of innovation, collaboration, and technical excellence. Qualifications Basic Qualifications: Master's degree or Ph.D in Computer Science, Robotics, Electrical Engineering, AI, or a closely related field with a focus on autonomous systems 10+ years of experience in software development and system engineering for autonomous driving or ADAS applications Proven industry experience in releasing AI-based L2+ systems, with a strong track record of successful product deployments Deep knowledge of E2E AI stack solutions and training algorithms, including reinforcement learning, and imitation learning, as well as motion control and optimization techniques Deep knowledge of AI frameworks such as TensorFlow and PyTorch Deep knowledge in model optimization and embedded deployment of E2E AI stacks to embedded automotive hardware Deep knowledge of cloud-based scalable training pipelines, MLOps, and CICD for training AI models with large-scale fleet datasets Proven track record of leading the end-to-end development and successful deployment of complex AI-powered systems into production environments at scale Preferred Qualifications: Experience with simulation tools and testing methodologies (SIL/HIL) for autonomous systems. Proficiency in programming languages such as Python and C++. Strong understanding of automotive safety standards (ISO 26262, ASIL) and regulatory requirements for ADAS. Exceptional leadership and communication skills, with the ability to inspire and manage cross-functional teams. Strategic thinker with strong problem-solving abilities and a passion for innovation in autonomous driving technology. Additional Information The U.S. base salary range for this full-time position is $240,000 - $320,000. Within the range, individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc. This range does not include annual bonus percentage nor any other monetary considerations for the total compensation package. Your Recruiter can share more details about the specific salary range for this position during the interview process. In addition to your base salary, Bosch offers a comprehensive benefits package that includes health, dental, and vision plans; health savings accounts (HSA); flexible spending accounts; 401(K) retirement plan with an attractive employer match; wellness programs; life insurance; long term disability insurance; paid time off; parental leave. Pay ranges included in the postings, when included, generally reflect base salary; certain positions may include bonus, or additional benefits. Equal Opportunity Employer, including disability / veterans Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.
Company Description "Invented for Life" drives us at Bosch and our vision of future mobility. Autonomous vehicles will change the way we move and at Bosch we are working on making this future a reality. We are now growing our team to solve some of the hardest automated driving problems, and are looking for experts for product engineering of AI-based Autonomous Driving systems. Job Description As the Senior Principal Engineer, E2E AI Training Framework for Autonomous Driving Systems, you will spearhead the development and optimization of the machinery that efficiently trains, optimizes, tests, and releases various E2E AI-based products (e.g. parking, driving, interior sensing) for different trim levels (entry, mid and high) within our L2+ ADAS stack. You will be responsible for defining and driving the technological roadmap for the AI machinery, ensuring its scalability, reliability, and performance. The ideal candidate must have prior industry experience in releasing AI-based L2+ systems, with a proven track record of translating innovative research into production-grade solutions. Key Responsibilities Define and drive execution of the technical roadmap and strategy for the E2E AI machinery, including training pipelines, optimization techniques, simulation and MLOps tooling. Oversee the design, development, and testing of the E2E AI machinery and its interaction with data sources, model repositories, and development targets. Collaborate closely with other functional tech leads (e.g. data engineering, infrastructure) to define and drive the overall architecture of the AI machinery ecosystem. Guide the set-up of a development framework that enables fast evaluation and integration of emerging E2E AI solutions. Guide the transition from research prototypes to production-ready solutions, ensuring performance optimization on automotive-grade hardware and scalability. Leverage your prior industry experience in launching AI-based L2+ systems to implement best practices in system validation, testing (SIL/HIL), and continuous improvement. Mentor and lead a high-caliber team of AI scientists and engineers, fostering a culture of innovation, collaboration, and technical excellence. Qualifications Basic Qualifications: Master's degree or Ph.D in Computer Science, Robotics, Electrical Engineering, AI, or a closely related field with a focus on autonomous systems 10+ years of experience in software development and system engineering for autonomous driving or ADAS applications Proven industry experience in releasing AI-based L2+ systems, with a strong track record of successful product deployments Deep knowledge of E2E AI stack solutions and training algorithms, including reinforcement learning, and imitation learning, as well as motion control and optimization techniques Deep knowledge of AI frameworks such as TensorFlow and PyTorch Deep knowledge in model optimization and embedded deployment of E2E AI stacks to embedded automotive hardware Deep knowledge of cloud-based scalable training pipelines, MLOps, and CICD for training AI models with large-scale fleet datasets Proven track record of leading the end-to-end development and successful deployment of complex AI-powered systems into production environments at scale Preferred Qualifications: Experience with simulation tools and testing methodologies (SIL/HIL) for autonomous systems. Proficiency in programming languages such as Python and C++. Strong understanding of automotive safety standards (ISO 26262, ASIL) and regulatory requirements for ADAS. Exceptional leadership and communication skills, with the ability to inspire and manage cross-functional teams. Strategic thinker with strong problem-solving abilities and a passion for innovation in autonomous driving technology. Additional Information The U.S. base salary range for this full-time position is $240,000 - $320,000. Within the range, individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc. This range does not include annual bonus percentage nor any other monetary considerations for the total compensation package. Your Recruiter can share more details about the specific salary range for this position during the interview process. In addition to your base salary, Bosch offers a comprehensive benefits package that includes health, dental, and vision plans; health savings accounts (HSA); flexible spending accounts; 401(K) retirement plan with an attractive employer match; wellness programs; life insurance; long term disability insurance; paid time off; parental leave. Pay ranges included in the postings, when included, generally reflect base salary; certain positions may include bonus, or additional benefits. Equal Opportunity Employer, including disability / veterans Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.
