Senior Lead Machine Learning Engineer

Capital One - Norfolk, VA

Full-time - Senior

Norfolk, VA

Credit Intermediation and Related Activities

About the position

As a Senior Lead Machine Learning Engineer at Capital One, you will be part of an Agile team focused on the design, development, and implementation of machine learning applications at scale. This role involves collaborating with cross-functional teams to solve complex business problems through machine learning, ensuring high availability and performance of applications, and adhering to best practices in Responsible and Explainable AI.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems.
• Inform ML infrastructure decisions using understanding of ML modeling techniques and issues.
• 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 for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Leverage continuous integration and continuous deployment best practices to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities and models are well-governed from a risk perspective.

Requirements

• Bachelor's degree
• At least 8 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 3 years of experience building, scaling, and optimizing ML systems
• At least 2 years of experience leading teams developing ML solutions

Nice-to-haves

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 4+ years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 3+ years of experience developing performant, resilient, and maintainable code
• 3+ years of experience with data gathering and preparation for ML models
• 3+ years of people management experience
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
• 3+ years of experience building production-ready data pipelines that feed ML models
• Ability to communicate complex technical concepts clearly to a variety of audiences

Benefits

• Comprehensive health benefits
• Financial benefits
• Inclusive set of benefits supporting total well-being