Senior Lead Machine Learning Engineer

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

Senior Lead Machine Learning Engineer at Capital One, focusing on productionizing ML applications and systems at scale. Responsibilities include designing, building, and delivering ML models and components, informing ML infrastructure decisions, writing and testing application code, automating tests and deployment, collaborating in Agile teams, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, implementing CI/CD best practices, and ensuring code security, model governance, and Responsible/Explainable AI. The role requires significant experience in building, scaling, and optimizing ML systems and leading 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

  • 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
  • leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • building, scaling, and optimizing ML systems
  • 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
  • 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