Sr. Lead Machine Learning Engineer (ic)

Capital One Capital One · Banking · McLean, VA +2

This role is for a Sr. Lead Machine Learning Engineer (IC) at Capital One, focused on productionizing ML applications and systems at scale within a fintech domain. The role involves designing, building, and delivering ML models, managing ML infrastructure, developing and validating models, automating tests and deployment, and maintaining models in production. It also includes leveraging cloud architectures, constructing data pipelines, and ensuring responsible and explainable AI practices. The role requires experience in building, scaling, and optimizing ML systems and leading teams in ML solution development.

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

Nice to have

  • AWS
  • Azure
  • Google Cloud Platform
  • scikit-learn
  • PyTorch
  • Dask
  • Spark
  • XGboost
  • 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, utilizing capabilities beyond basic code completion

What the JD emphasized

  • at scale
  • production
  • production
  • production
  • production
  • production
  • production
  • production
  • production
  • production

Other signals

  • productionizing machine learning applications and systems at scale
  • develop and review model and application code
  • ensure high availability and performance of our machine learning applications
  • continuously learn and apply the latest innovations and best practices in machine learning engineering
  • design, build, and/or deliver ML models and components that solve real-world business problems
  • retrain, maintain, and monitor models in production
  • leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
  • construct optimized data pipelines to feed ML models
  • leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
  • 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