Staff Machine Learning Engineer - Rider Intelligence

Uber Uber · Consumer · Seattle, WA · Engineering

Staff ML Engineer at Uber focused on Rider Intelligence, responsible for defining and executing technical strategies, leading the design, development, and production of end-to-end ML solutions for large-scale distributed systems, and mentoring a team of MLEs. Requires significant experience in ML model deployment and expertise in areas like search, recommendation systems, and ranking.

What you'd actually do

  1. Define and execute technical strategies, spanning from model and system architecture to business objectives and stakeholder alignment.
  2. Lead the design, development, and production of end-to-end ML solutions for large-scale distributed systems serving billions of trips.
  3. Lead and mentor a team of Machine Learning Engineers (MLEs), providing technical leadership, setting the vision, and guiding the team through the full development lifecycle—from ideation to model deployment and scaling.

Skills

Required

  • Ph.D., M.S. or Bachelor in Computer Science, Mathematics with focus on Machine Learning, or equivalent technical background
  • 8+ years experience leading the development and deployment of ML models in large-scale production environments

Nice to have

  • Expertise in search, recommendation systems, ranking/retrieval, or representation learning
  • Proven experience in ranking optimization across heterogeneous content types.
  • Demonstrated success in leading cross-functional projects

What the JD emphasized

  • exceptional demonstrated impact
  • leading the development and deployment of ML models in large-scale production environments
  • Expertise in search, recommendation systems, ranking/retrieval, or representation learning is highly desirable.
  • Proven experience in ranking optimization across heterogeneous content types.
  • Demonstrated success in leading cross-functional projects that deliver significant business impact.

Other signals

  • ML models
  • production environments
  • large-scale distributed systems