Sr Lead Machine Learning Engineer

Capital One Capital One · Banking · New York, NY

This role focuses on the engineering aspects of machine learning, specifically in productionizing ML applications and systems at scale within a financial services context. The engineer will be involved in the design, development, implementation, and maintenance of ML models and infrastructure, ensuring high availability and performance. The role emphasizes collaboration, code quality, and leveraging cloud platforms for ML deployment.

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

  • 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

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • high availability and performance
  • ML infrastructure decisions
  • model training
  • automating tests and deployment
  • monitor models in production
  • optimized ML models at scale
  • optimized data pipelines to feed ML models
  • test automation and monitoring
  • Responsible and Explainable AI

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