Lead Machine Learning Engineer

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

Lead Machine Learning Engineer at Capital One focused on productionizing ML applications and systems at scale. Responsibilities include designing, building, and delivering ML models, optimizing ML infrastructure, writing and testing code, automating tests and deployment, and maintaining models in production. The role emphasizes cloud-based architectures, data pipelines, CI/CD, and responsible AI practices.

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
  • industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
  • 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

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
  • leverage continuous integration and continuous deployment best practices
  • 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