Sr. Distinguished Machine Learning Engineer

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

Senior Distinguished Machine Learning Engineer role focused on technical leadership for productionizing ML applications and systems at scale within the financial services industry. The role involves driving the creation and evolution of ML models and software, leveraging cloud architectures, optimizing data pipelines, and mentoring other engineers. Requires extensive experience in data-intensive solutions, ML development lifecycle, and programming.

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

  • designing and building data-intensive solutions using distributed computing
  • programming in C, C++, Python, or Scala
  • full ML development lifecycle using modern technology in a business critical setting

Nice to have

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

What the JD emphasized

  • full ML development lifecycle using modern technology in a business critical setting
  • production-ready data pipelines that feed ML models
  • scaling production-ready data pipelines

Other signals

  • productionizing machine learning applications and systems at scale
  • technical leadership
  • solve challenging business problems in the financial services industry
  • state-of-the-art intelligent systems
  • Leverage cloud-based architectures and technologies to deliver optimized ML models at scale