Lead Machine Learning Engineer at Capital One supports horizontal AI enablement for Finance technology teams, building reusable machine learning engineering practices and end user facing AI capabilities for Finance LOB. This onsite role in New York, NY pairs deep ML systems work with production responsibilities, spanning model evaluation, large language model inference, similarity search, guardrails, governance, observability, agentic AI, and ongoing model monitoring.
You will help design and deliver AI-powered products that affect how associates work while also creating customer value. The work is done with a cross-functional group of engineers, data scientists, product managers, and designers, using an Agile approach to build and improve software that applies state-of-the-art AI and ML capabilities.
What you’ll do
- Partner across engineering, data science, product, and design teams to deliver AI-powered products for associates and customers.
- Design, develop, test, deploy, and support AI software components that incorporate model evaluation and experimentation, LLM inference, similarity search, guardrails, governance, observability, and agentic AI.
- Fine-tune, develop, and evaluate machine learning and foundation models.
- Contribute to a long-term AI roadmap by sharing thought leadership and technical vision for pioneering AI systems.
- Apply and integrate a broad stack of Open Source and SaaS AI technologies.
- Use ML modeling knowledge to inform ML infrastructure decisions, including practical considerations and known ML issues.
- Retrain, maintain, and monitor models in production.
- Construct optimized data pipelines to feed ML models.
- Manage code to reduce vulnerabilities, ensure risk-governed models, and follow Responsible and Explainable AI best practices.
Skills and qualifications
- Bachelor’s Degree
- 6+ years experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
- 4+ years programming with Python, Scala, or Java
- 2+ years building, scaling, and optimizing ML systems
Technologies you’ll work with: Python, Scala, Java, scikit-learn, PyTorch, Dask, Spark, TensorFlow, Retrieval Augmented Generation (RAG), AWS Bedrock, Google Cloud, Azure, plus Open Source and SaaS AI tools.
Benefits
- Comprehensive, competitive, and inclusive health, financial, and other benefits that support total well-being.
- Performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).
Compensation: USD 215,200 - 245,600 per year.
