Lead Machine Learning Engineer
In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities.
In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value.
What You'll Do:The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
- Partner with a cross-functional team of engineers, data scientists, product managers, and designers to deliver AI-powered products that change how our associates work and provide value to our customers.
- Design, develop, test, deploy, and support AI software components utilizing 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 your ML infrastructure decisions using your 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.
The Ideal Candidate:- You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.
- You are a passionate communicator, comfortable with explaining complex technical concepts to non-technical partners across the business, sometimes in front of large audiences.
- Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.
- You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven.
- You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss.
- You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.
- Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production
Strategic & Business-Oriented:You think beyond the technology, deeply understanding business needs and how AI can solve them. You don't just build; you strategize and prioritize work that delivers the greatest business value.
Highly Collaborative & Transparent: You are a natural partner, working seamlessly across engineering, product, and data science teams. You communicate your progress, blockers, and decisions clearly and proactively, ensuring everyone is aligned. You love sharing knowledge and insights and are committed to the success of the entire team.
Technically Mature & Humble: You possess a strong foundation in engineering and mathematics. You are a resilient problem-solver who can bring clarity to complex, undefined problems and articulate your findings concisely. You demonstrate professional maturity by committing to and executing on team decisions.
Flexible & Fungible:You are eager to roll up your sleeves and contribute wherever the team needs you most. You are comfortable working across different aspects of the tech stack and adapting to evolving priorities.
A Lifelong Learner: You love staying current with the latest AI research and can apply novel techniques to production systems judiciously, always with a focus on business impact.
Basic Qualifications:- 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 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
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer
McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer
Richmond, VA: $179,400 - $204,700 for Lead Machine Learning Engineer
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please.
