Senior Manager, Machine Learning Engineering

Capital One Capital One · Banking · McLean, VA +1

Senior Manager, Machine Learning Engineering 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, optimizing data pipelines, and ensuring the performance and availability of ML applications in production. It requires people management experience and collaboration with cross-functional Agile teams.

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

  • Bachelor’s Degree
  • 8 years of experience designing and building data-intensive solutions using distributed computing
  • 4 years of experience programming with Python, Scala, or Java
  • 3 years of experience building, scaling, and optimizing ML systems
  • 2 years of experience leading teams developing ML solutions
  • 4 years of people management experience

Nice to have

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
  • 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
  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • 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
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

What the JD emphasized

  • At least 2 years of experience leading teams developing ML solutions
  • At least 4 years of people management experience

Other signals

  • productionizing machine learning applications and systems at scale
  • design, build, and/or deliver ML models and components that solve real-world business problems
  • 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
  • Retrain, maintain, and monitor models in production