Staff Machine Learning Engineer - Rider Intelligence

Staff Machine Learning Engineer - Rider Intelligence Department: Engineering Team: Machine Learning Location: Seattle, United States Type: Full-Time **About the Team** There are many different types of users, opening the app in many contexts, and we need to match them to the many services and content we have available. Rider Experience drives and enables the critical trip booking funnel within the Rides app that makes up almost all of the trip transactions and contributes tremendously to business growth. We actively explore algorithmic improvements to how we help millions of riders find and discover the right products every hour to move around the world, and power smart and intuitive experiences for them. **About the Role** Staff Machine Learning Engineers  at Uber are passionate and pragmatic technologists who are able to translate business insight and goals into well-formulated ML projects and scalable solutions to deliver impact. They are not only collaborative role models but also approachable leaders, humble teachers while also effective in helping the team in project execution. You will work with talented people in product, science, operations, and platform teams to help build and optimize our Rider Experience products. The role requires technical chops as well as strong communication & leadership skills. **What You'll Do** - Define and execute technical strategies, spanning from model and system architecture to business objectives and stakeholder alignment. - Lead the design, development, and production of end-to-end ML solutions for large-scale distributed systems serving billions of trips. - 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. **Basic Qualifications** - Ph.D., M.S. or Bachelor in Computer Science, Mathematics with focus on Machine Learning, or equivalent technical background with exceptional demonstrated impact - 8+ years experience leading the development and deployment of ML models in large-scale production enviro