Sr. Lead Machine Learning Engineer

Capital One Capital One · Banking · McLean, VA +3

Senior Lead Machine Learning Engineer at Capital One, focusing on productionizing ML applications and systems at scale within a fintech domain. The role involves designing, building, and delivering ML models, managing ML infrastructure, writing and testing application code, automating deployments, and maintaining/monitoring models in production. It emphasizes leveraging cloud-based architectures and optimizing data pipelines for ML models, with a strong focus on CI/CD and Responsible AI practices. The role also involves leading teams and potentially managing people.

What you'd actually do

  1. Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
  2. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)
  3. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
  4. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
  5. Retrain, maintain, and monitor models in production

Skills

Required

  • Python
  • Scala
  • Java
  • designing and building data-intensive solutions using distributed computing
  • building, scaling, and optimizing ML systems
  • leading teams developing ML solutions
  • cloud-based architectures
  • CI/CD best practices
  • test automation
  • monitoring
  • Responsible and Explainable AI

Nice to have

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
  • developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • scikit-learn
  • PyTorch
  • Dask
  • Spark
  • TensorFlow
  • developing performant, resilient, and maintainable code
  • data gathering and preparation for ML models
  • people management experience
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
  • building production-ready data pipelines that feed ML models
  • communicate complex technical concepts clearly to a variety of audiences
  • leveraging interactive AI tooling to accelerate productivity

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • designing and building data-intensive solutions using distributed computing
  • building, scaling, and optimizing ML systems
  • leading teams developing ML solutions
  • Responsible and Explainable AI

Other signals

  • productionizing machine learning applications and systems at scale
  • design, build, and/or deliver ML models and components
  • Retrain, maintain, and monitor models in production
  • Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale