Capital One is hiring a Lead Machine Learning Engineer for Finance Tech in AI Enablement, an onsite role based in McLean, VA 22101. In this position, you will lead the development and deployment of AI and machine learning capabilities, focusing on best practices and end user-facing use cases that support how Finance Line of Business associates work while delivering value to customers.
You will work across engineering, data science, product, and design teams to build, evaluate, deploy, monitor, and govern AI/ML systems. The role also emphasizes production readiness, risk-aware governance, and ongoing model performance through retraining and observability.
What you’ll do
- Collaborate with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products for associates and customers.
- Design, develop, test, deploy, and support AI software components that use machine learning models, including model evaluation and experimentation.
- Build capabilities spanning large language model inference, similarity search, guardrails, governance, observability, and agentic AI.
- Fine-tune, develop, and evaluate machine learning and foundation models.
- Contribute technical vision and thought leadership toward the long-term roadmap for pioneering AI systems at Capital One.
- Use a broad stack of open source and SaaS AI technologies to solve practical problems.
- Apply understanding of ML modeling techniques and issues to inform ML infrastructure decisions.
- Retrain, maintain, and monitor models in production, including ongoing performance oversight.
- Construct optimized data pipelines to supply ML models.
- Ensure code quality and risk management by maintaining well-governed models and following Responsible and Explainable AI best practices.
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
Preferred qualifications
- Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
- 7+ years of experience designing, developing, delivering, and supporting AI services at scale
- 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
- 3+ years of experience developing AI and ML algorithms or technologies using Python
- 2+ years of experience with Retrieval Augmented Generation (RAG)
- 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 designing, implementing, and scaling complex data pipelines for ML models and evaluating performance
- Experience leveraging interactive AI tooling to accelerate productivity, using capabilities beyond basic code completion
- Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, or Azure
Technologies
- Python, Scala, Java
- scikit-learn, PyTorch, TensorFlow
- Dask, Spark
- Retrieval Augmented Generation (RAG)
- AWS Bedrock, Google Cloud, Azure
Compensation and benefits
- Salary range: $197,300 - $225,100 per year (McLean, VA onsite)
- Eligible to earn performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI).
- Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
Salary by location (annual): Cambridge, MA: $197,300 - $225,100; McLean, VA: $197,300 - $225,100; New York, NY: $215,200 - $245,600. Candidates hired in other locations will receive the pay range associated with that location, and the actual annualized salary offered will be reflected in the candidate’s offer letter.
