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Overview
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you\'ll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You\'ll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you\'ll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages such as Python, Scala, or Java. Basic Qualifications
Bachelor’s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems 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 Additional information
This role is not open to sponsorship for employment authorization or immigration-related support (e.g., H1B, OPT, STEM OPT, CPT, J-1, TN, E-2, E-3, L-1, O-1, or any EADs) from Capital One. The minimum and maximum full-time annual salaries for this role are listed below, by location. Salaries for part-time roles will be prorated based on hours worked. Locations and ranges include: Cambridge, MA: $193,400 - $220,700 Chicago, IL: $175,800 - $200,700 McLean, VA: $193,400 - $220,700 New York, NY: $211,000 - $240,800 Richmond, VA: $175,800 - $200,700 San Francisco, CA: $211,000 - $240,800 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 will be reflected in the offer letter. This role is also eligible to earn performance-based incentive compensation, which may include cash bonuses 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 total well-being. 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 /vet) committed to non-discrimination in compliance with applicable laws. Capital One promotes a drug-free workplace. Capital One will consider qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries. If you require an accommodation in the application process, contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information will be kept confidential and used only to provide needed reasonable accommodations. For technical support or questions about Capital One\'s recruiting process, please email Careers@capitalone.com. Capital One does not provide, endorse nor guarantee third-party products or services and is not liable for information available through this site. Capital One Financial is made up of several entities. Positions posted in Canada are for Capital One Canada; in the United Kingdom, for Capital One Europe; and in the Philippines, for Capital One Philippines Service Corp. (COPSSC).
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Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you\'ll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You\'ll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. What you’ll do in the role
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you\'ll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages such as Python, Scala, or Java. Basic Qualifications
Bachelor’s degree At least 6 years of experience designing and building data-intensive solutions using distributed computing At least 4 years of experience programming with Python, Scala, or Java At least 2 years of experience building, scaling, and optimizing ML systems 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 Additional information
This role is not open to sponsorship for employment authorization or immigration-related support (e.g., H1B, OPT, STEM OPT, CPT, J-1, TN, E-2, E-3, L-1, O-1, or any EADs) from Capital One. The minimum and maximum full-time annual salaries for this role are listed below, by location. Salaries for part-time roles will be prorated based on hours worked. Locations and ranges include: Cambridge, MA: $193,400 - $220,700 Chicago, IL: $175,800 - $200,700 McLean, VA: $193,400 - $220,700 New York, NY: $211,000 - $240,800 Richmond, VA: $175,800 - $200,700 San Francisco, CA: $211,000 - $240,800 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 will be reflected in the offer letter. This role is also eligible to earn performance-based incentive compensation, which may include cash bonuses 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 total well-being. 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 /vet) committed to non-discrimination in compliance with applicable laws. Capital One promotes a drug-free workplace. Capital One will consider qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries. If you require an accommodation in the application process, contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information will be kept confidential and used only to provide needed reasonable accommodations. For technical support or questions about Capital One\'s recruiting process, please email Careers@capitalone.com. Capital One does not provide, endorse nor guarantee third-party products or services and is not liable for information available through this site. Capital One Financial is made up of several entities. Positions posted in Canada are for Capital One Canada; in the United Kingdom, for Capital One Europe; and in the Philippines, for Capital One Philippines Service Corp. (COPSSC).
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