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Overview
Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you will be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on ML architectural design, develop and review model and application code, and ensure high availability and performance of ML 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. You will perform 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, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions using understanding of ML modeling techniques and issues, including model choice, 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 platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Adopt continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code quality and governance, manage risks, and apply Responsible and Explainable AI practices.
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 ML frameworks 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 leadership experience
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Experience deploying ML solutions in a public cloud (AWS, Azure, or GCP)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Impact in the ML industry 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 immigration related support for this position (e.g., H1B, OPT, STEM OPT, CPT, J-1, TN, or other work authorization).
Compensation and Benefits 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. Salaries refer to the amount Capital One is willing to pay at the time of posting.
McLean, VA: $193,400 - $220,700 for Lead Machine Learning Engineer
Richmond, VA: $175,800 - $200,700 for Lead Machine Learning Engineer
Candidates hired in other locations will follow the pay range for that location.
This role is eligible for 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 set of health, financial, and other benefits. Eligibility varies based on status. For more details, visit the Capital One Careers website.
Application and Equal Opportunity This role is expected to accept applications for a minimum of 5 business days. Capital One is an equal opportunity employer (EOE, including /vet) committed to non-discrimination in accordance with applicable laws. Capital One promotes a drug-free workplace and will consider qualified applicants with a criminal history in a manner consistent with the law.
If you require an accommodation during the application process, please contact Capital One Recruiting at 1-800-304-9102 or email RecruitingAccommodation@capitalone.com. For questions about Capital One's recruiting process, please email Careers@capitalone.com.
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Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you will be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on ML architectural design, develop and review model and application code, and ensure high availability and performance of ML 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. You will perform 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, in collaboration with Product and Data Science teams.
Inform ML infrastructure decisions using understanding of ML modeling techniques and issues, including model choice, 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 platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Adopt continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
Ensure code quality and governance, manage risks, and apply Responsible and Explainable AI practices.
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 ML frameworks 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 leadership experience
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Experience deploying ML solutions in a public cloud (AWS, Azure, or GCP)
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Impact in the ML industry 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 immigration related support for this position (e.g., H1B, OPT, STEM OPT, CPT, J-1, TN, or other work authorization).
Compensation and Benefits 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. Salaries refer to the amount Capital One is willing to pay at the time of posting.
McLean, VA: $193,400 - $220,700 for Lead Machine Learning Engineer
Richmond, VA: $175,800 - $200,700 for Lead Machine Learning Engineer
Candidates hired in other locations will follow the pay range for that location.
This role is eligible for 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 set of health, financial, and other benefits. Eligibility varies based on status. For more details, visit the Capital One Careers website.
Application and Equal Opportunity This role is expected to accept applications for a minimum of 5 business days. Capital One is an equal opportunity employer (EOE, including /vet) committed to non-discrimination in accordance with applicable laws. Capital One promotes a drug-free workplace and will consider qualified applicants with a criminal history in a manner consistent with the law.
If you require an accommodation during the application process, please contact Capital One Recruiting at 1-800-304-9102 or email RecruitingAccommodation@capitalone.com. For questions about Capital One's recruiting process, please email Careers@capitalone.com.
#J-18808-Ljbffr