Lead Machine Learning Engineer, Card Tech

Capital One Capital One · Banking · New York, NY

Lead Machine Learning Engineer at Capital One focused on productionizing ML applications and systems at scale within the Card Tech division. Responsibilities include designing, building, and delivering ML models, informing ML infrastructure decisions, writing and testing application code, collaborating in Agile teams, retraining/maintaining/monitoring production models, leveraging cloud architectures, constructing data pipelines, and ensuring CI/CD best practices, code quality, and responsible AI.

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

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
  • building production-ready data pipelines that feed ML models
  • industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or 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
  • 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
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
  • 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
  • build, and/or deliver ML models
  • model training
  • validating ML models
  • automating tests and deployment
  • optimized ML models at scale
  • optimized data pipelines
  • test automation
  • deployment of ML models
  • Responsible and Explainable AI
  • scaling ML systems

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