Lead Machine Learning Engineer

Benefits

• Health benefits
• Financial benefits
• Other benefits
• Performance-based incentive compensation (cash bonuses and/or long-term incentives)

This onsite opportunity in Richmond, VA supports collaborative, impact-driven work in Capital One's Risk Tech group, with a focus on responsible and explainable AI and opportunities to deliver AI powered products that matter for associates and customers.

Responsibilities

• Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI powered products that transform how associates work and deliver value to customers.
• Design, develop, test, deploy, and support AI software components using machine learning models, including model evaluation and experimentation, large language model inference, similarity search, guardrails, governance, observability, and agentic AI.
• Fine-tune, develop, and evaluate machine learning and foundation models.
• Collaborate as part of a cross-functional Agile team to create and enhance software that utilizes state-of-the-art AI and ML capabilities.
• Contribute thought leadership and technical vision to the long term roadmap of pioneering AI systems at Capital One.
• Leverage a broad stack of Open Source and SaaS AI technologies.
• Inform ML infrastructure decisions using an understanding of ML modeling techniques and issues.
• Retrain, maintain, and monitor models in production.
• Construct optimized data pipelines to feed ML models.
• 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.

Requirements

• 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 ML systems