Distinguished Machine Learning Engineer

Capital One Capital One · Banking · Plano, TX +1

Distinguished Machine Learning Engineer role focused on providing technical leadership for productionizing ML applications and systems at scale within the financial services industry. The role involves designing, developing, and implementing ML applications, optimizing data pipelines, and mentoring other engineers, leveraging cloud architectures for scalable ML model delivery.

What you'd actually do

  1. Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams
  2. Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems
  3. Lead large-scale ML initiatives with the customer in mind
  4. Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
  5. Optimize data pipelines to feed ML models

Skills

Required

  • Bachelor's degree
  • 10 years of experience designing and building data-intensive solutions using distributed computing
  • 6 years of experience programming in C, C++, Python, or Scala
  • 3 years of experience with the full ML development lifecycle using modern technology in a business critical setting

Nice to have

  • Master's Degree
  • 3+ years of experience designing, implementing, and scaling production-ready data pipelines that feed ML models
  • 2+ years of experience using Dask, RAPIDS, or in High Performance Computing
  • 2+ years of experience with the PyData ecosystem (NumPy, Pandas, and Scikit-learn)
  • Ability to communicate complex technical concepts clearly to a variety of audiences
  • ML industry impact through conference presentations, papers, blog posts, or open source contributions
  • Ability to attract and develop high-performing software engineers with an inspiring leadership style

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • full ML development lifecycle using modern technology in a business critical setting
  • scaling production-ready data pipelines that feed ML models

Other signals

  • productionizing machine learning applications and systems at scale
  • technical leadership to engineering teams
  • deliver ML models and software components
  • state-of-the-art intelligent systems
  • Leverage cloud-based architectures and technologies to deliver optimized ML models at scale