Lead Machine Learning Engineer
$197,300–$225,100 year
On-siteRichmond, Virginia, United States
Job Summary
Design, develop, test, deploy, and support AI software components utilizing machine learning models, including model evaluation, experimentation, large language model inference, and agentic AI. Partner with cross-functional teams to build proprietary Risk management solutions powered by state-of-the-art AI, fine-tune foundation models, and construct optimized data pipelines. Retrain, maintain, and monitor models in production while ensuring code is well-managed, models are governed from a risk perspective, and systems follow best practices in Responsible and Explainable AI. Contribute thought leadership to the long-term roadmap of pioneering AI systems at Capital One, leveraging a broad stack of Open Source and SaaS technologies. Lead teams developing ML solutions and communicate complex technical concepts to non-technical partners across the business.
Required Qualifications
- Bachelor's Degree
- At least 6 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 2 years of experience building, scaling, and optimizing ML systems
Desired 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 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 their performance
- Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion
- Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure
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