Staff Machine Learning Engineer - Moonshot AI

Staff Machine Learning Engineer - Moonshot AI Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the role** Uber AI Solutions is one of Uber’s biggest bets with the ambition to build one of the world’s largest data foundries for AI applications and evolve into a platform of choice for a variety of online tasks. The Moonshot AI team focuses on optimizing the Uber AI Solutions gig marketplace through intelligent supply and demand matching. We also accelerate human-in-the-loop data annotation with automation and develop robust automated evaluation systems. We are in the early stages, with significant opportunities to build new ML models for the gig marketplace. This includes integrating advanced ML models to enable ML-assisted annotations for various use cases, such as Gen AI Labeling, Image/Audio/Video Classification, and Image/Video Segmentation, building LLM-as-Judge for automated quality evaluation, training ranking models to recommend gigs to earners, etc.. In this role, you will collaborate closely with product managers, program managers, and cross-functional teams to deliver real world impact. You’ll help grow Uber AI Solutions into a leader in the space. **What the candidate will do:** - Contribute to and influence the technical direction for Uber AI Solutions, particularly around ML applications and research. - Identify and choose the right problems that will benefit from ML expertise. - Design and develop a suite of ML models that will help solve the above problems. - Collaborate with backend and frontend engineers to integrate your solutions into our products & platforms. - Explore novel ideas and innovative solutions that can lead to a step change in our products - Work with cross-functional counterparts like the ML Ops team to understand their needs and improve the models accordingly. - Provide mentorship to engineers on the team and across partner orgs to help raise the technical bar. **Basic Qualifications** 1. Ph.D., MS, or Bachelors degree in Computer Science or a closely related discipline. 2. Proficiency in Computer Vision (CV), N