Lead Machine Learning Engineer (enterprise Platforms Technology)

Capital One Capital One · Banking · New York, NY +1

Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, including designing, building, and delivering ML models, optimizing ML infrastructure, developing data pipelines, and maintaining models in production within a cloud-based environment. The role emphasizes engineering best practices for ML systems.

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
  • building production-ready data pipelines that feed ML models
  • developing performant, resilient, and maintainable code
  • data gathering and preparation for ML models
  • developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

Nice to have

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
  • people leader experience
  • leading teams developing ML solutions using industry best practices, patterns, and automation
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
  • scikit-learn
  • PyTorch
  • Dask
  • Spark
  • TensorFlow

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • building, scaling, and optimizing ML systems
  • building production-ready data pipelines that feed ML models
  • developing and deploying ML solutions in a public cloud
  • designing, implementing, and scaling complex data pipelines for ML models

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
  • Construct optimized data pipelines to feed ML models