Build and productionize large-scale reinforcement learning to power personalized customer experiences. Capital One is hiring a Senior Lead Machine Learning Engineer in McLean, VA (onsite) to design, build, deploy, and monitor recommender systems across Mobile, Web, and Email. The role combines ML engineering, cloud architecture, and operational excellence, with a team focused on reusable capabilities for enterprise experimentation.
What you’ll do
- Design, build, and deliver machine learning models and components that address real business needs in collaboration with Product and Data Science
- Shape ML infrastructure decisions based on modeling fundamentals, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Handle complex technical problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Partner within a cross-functional Agile team to create and improve software enabling state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production to keep systems effective over time
- Use or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
- Construct optimized data pipelines that feed ML models
- Apply CI/CD best practices, including test automation and monitoring, to support reliable deployment of ML models and application code
- Ensure secure, well-managed code, strong governance from a risk perspective, and alignment with Responsible and Explainable AI best practices
- Develop using Python, Scala, or Java
Team and impact
Within Card Tech, the Customer Intelligent Decisions & Experiences (CIDX) team is building the next generation of large-scale reinforcement learning-based recommender systems. These systems power personalized experiences across marketing, customer servicing, and digital products for millions of Card and MainStreet customers.
The team also contributes reusable capabilities to a Capital One-wide Experimentation Platform, helping users across the enterprise apply machine learning to a range of use cases.
Minimum qualifications
- Bachelor’s Degree
- 8+ years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
- 4+ years of programming experience with Python, Scala, or Java
- 3+ years building, scaling, and optimizing ML systems
- 2+ years leading teams developing ML solutions
Preferred qualifications
- Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
- Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
- 4+ 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 performant, resilient, and maintainable code
- 3+ years of experience with data gathering and preparation for ML models
- 3+ years of people management experience
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- 3+ years of experience building production-ready data pipelines that feed ML models
- Ability to communicate complex technical concepts clearly to a variety of audiences
- Experience leveraging interactive AI tooling to accelerate productivity, using capabilities beyond basic code completion
Tools you may use
- Python, Scala, Java
- AWS, Azure, Google Cloud Platform
- scikit-learn, PyTorch, Dask, Spark, TensorFlow
Compensation and benefits
The annual salary range for this role is USD 229,900 - 262,400. This position is also eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
Capital One provides a comprehensive, competitive, and inclusive benefits package covering health, financial, and other support for your total well-being.
