Lead Machine Learning Engineer

As a Lead Machine Learning Engineer on an Agile team, you will design, build, and productionize machine learning applications at scale. The role focuses on ML architectural design, model and application code, and ongoing production monitoring and governance, with an emphasis on cloud-based distributed systems and Responsible and Explainable AI practices.

Role Summary

You will develop cloud-based and distributed machine learning solutions, including CI/CD-driven deployment, automated testing, and model monitoring. Work includes defining ML infrastructure approaches, constructing data pipelines, and ensuring models and code meet governance and risk best practices.

Responsibilities

• Design, build, and deliver machine learning models and components that address real-world business problems in collaboration with Product and Data Science teams.
• Apply expertise in ML modeling techniques and associated issues to guide infrastructure decisions, including model and data/feature selection, 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 within a cross-functional Agile team to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale, including AWS and Kubernetes.
• Construct optimized data pipelines that feed machine learning models.
• Use continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful releases of models and application code.
• Ensure code is well-managed to reduce vulnerabilities, support risk-governed model practices, and maintain Responsible and Explainable AI best practices.
• Use programming languages such as Python, Go, Scala, or Java.

Required 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 machine learning systems.

Preferred Qualifications

• Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a related field.
• 3+ years of experience building production-ready data pipelines that feed machine learning 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 using Python or Go.
• 2+ years of experience with data gathering and preparation for machine learning models.
• 2+ years of people leader experience.
• 1+ years of experience leading teams developing machine learning solutions using industry best practices, patterns, and automation.
• Experience developing and deploying machine learning solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
• Experience designing, implementing, and scaling infrastructure using Kubernetes.
• Experience designing, implementing, and scaling complex data pipelines for machine learning models and evaluating their performance.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
• Experience leveraging interactive AI tooling to accelerate productivity, using capabilities beyond basic code completion.

Technologies

• Python, Go, Scala, Java
• AWS, Kubernetes
• scikit-learn, PyTorch, Dask, Spark, TensorFlow
• Azure, Google Cloud Platform

Location

New York, NY (onsite)

Compensation

• USD 215,200 - 245,600 per year (New York, NY)
• Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

Additional Information

• 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 and veteran status) committed to non-discrimination in compliance with applicable federal, state, and local laws.
• Capital One promotes a drug-free workplace.
• Capital One will consider qualified applicants with a criminal history consistent with applicable laws regarding criminal background inquiries.
• If you require an accommodation to apply, contact Capital One Recruiting at 1-800-304-9102 or [email protected].
• For technical support or questions about Capital One’s recruiting process, email [email protected].
• Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site.

Immigration Authorization Statement

• Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position (including H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1, O-1, and other forms of work authorization requiring employer support).