Staff Machine Learning Engineer, Delivery Marketplace (Sunnyvale/San Francisco/NYC)

Staff Machine Learning Engineer, Delivery Marketplace (Sunnyvale/San Francisco/NYC) Department: Engineering Team: Machine Learning Location: New York, United States Type: Full-Time **About the Role** Uber’s Delivery Marketplace is at the heart of Uber’s Delivery business and the Logistics Prediction & Optimization team develops the ML models, OR algorithms, signals, and large-scale distributed systems that power real-time Eater Experience and real-time matching decisions for billions of trips. Engineers on the team work on cutting-edge marketplace ML problems and real-time multi-objective optimizations. The team regularly delivers $1B+ to Uber’s revenue growth and $XX M in profits. We are looking for exceptional Staff ML engineers with a track record of extraordinary impact and with a passion for building large-scale systems that optimize multi-sided real-time marketplaces. In this role, you will lead the design, development, and productionization of advanced ML models and matching algorithms, covering deep learning, causal modeling, and reinforcement learning. You will work with talented ML and BE engineers, product managers, and scientists to set the team’s technical direction and solve some of Uber’s most challenging and most complex business problems in order to deliver a magical experience to users improving reliability of Uber Delivery trips. **What You Will Do** 1. Drive technical strategy and roadmap ownership over a 1+ year horizon and own the implementation, including platform-level architecture decisions, executive communication and alignment, technical mentorship, and cross-team technical influence 2. Lead the design, development, and productionization of end-to-end ML solutions for large-scale distributed systems serving billions of trips 3. Develop novel products for online marketplaces combining machine learning for prediction and forecasting, and optimization to to improve business efficiency and the user experience. 4. Lead and mentor a team of MLEs, providing technical leadership, setting the vision, and guiding the team through the end-to-end development process — from ideation to model deployment and scaling. 5. Balance