Sr. Lead Machine Learning Engineer

Capital One Capital One · Banking · New York, NY +4

Senior Lead Machine Learning Engineer role focused on productionizing ML applications and systems at scale within a fintech company. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing code, automating tests and deployment, collaborating in Agile teams, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code quality, risk governance, and Responsible AI. Requires experience in data-intensive solutions, programming, scaling ML systems, and leading ML development 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
  • ML modeling techniques
  • model training
  • hyperparameter tuning
  • model validation
  • application code development
  • test automation
  • deployment automation
  • cloud-based architectures
  • data pipelines
  • CI/CD best practices
  • Responsible and Explainable AI

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
  • Kubeflow
  • TensorFlow
  • developing performant, resilient, and maintainable code
  • data gathering and preparation for ML models
  • 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
  • build, and/or deliver ML models
  • model training
  • validate ML models
  • automating tests and deployment
  • retrain, maintain, and monitor models in production
  • build cloud-based architectures
  • construct optimized data pipelines
  • test automation
  • deployment of ML models
  • Responsible and Explainable AI

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, including test automation and monitoring, to ensure successful deployment of ML models and application code