Lead Machine Learning Engineer

Capital One Capital One · Banking · McLean, VA

Lead Machine Learning Engineer at Capital One, focused on productionizing ML applications and systems at scale within an Agile team. Responsibilities include designing, building, and delivering ML models, informing infrastructure decisions, writing and testing code, automating tests and deployment, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring code quality, model governance, and adherence to Responsible and Explainable AI best 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

Nice to have

  • scikit-learn
  • PyTorch
  • Dask
  • Spark
  • TensorFlow
  • developing performant, resilient, and maintainable code
  • data gathering and preparation for ML models
  • people leader experience
  • leading teams developing ML solutions using industry best practices, patterns, and automation
  • AWS
  • Azure
  • Google Cloud Platform
  • designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
  • interactive AI tooling

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • build, and/or deliver ML models
  • model training
  • model code
  • ML applications
  • ML infrastructure
  • ML modeling techniques
  • ML applications
  • ML applications
  • ML models
  • ML models
  • ML models
  • ML models
  • ML models
  • ML solutions
  • ML solutions

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
  • 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