Machine Learning Engineer II

Machine Learning Engineer II Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the Role** Uber’s Marketplace is at the core of the business. The Earner Incentive team in Marketplace builds products and systems that empower drivers through targeted incentives, creating a more balanced and efficient marketplace while enhancing earnings and experience. The team owns the end-to-end incentive lifecycle, from ML-driven incentive generation to scalable online serving, answering questions such as _who, where, when, how, and how much, powered by large-scale machine learning, optimization, and experimentation systems_. These systems enable proactive, targeted incentives that shape supply, optimize earnings, and guide marketplace balance. We are seeking a Machine Learning Engineer to help build and scale the technical foundations behind Uber’s driver incentive systems. You will be responsible for developing and productionizing large-scale ML models and decision systems that power both scheduled and near real-time incentive generation. In this role, you will collaborate with senior engineers, product managers, and data scientists to implement technical solutions, navigate trade-offs, and maintain reliable production systems. Your work will directly impact marketplace efficiency and empower earning opportunities for millions of drivers worldwide. **What the Candidate Will Do** - Build, productionize, and maintain ML solutions and data pipelines for the large-scale systems that power Uber’s driver incentives. - Implement and iterate on advanced ML and optimization techniques to improve marketplace efficiency and reliability, directly impacting the earning opportunities of millions of drivers. - Translate business requirements into actionable technical tasks and practical, production-ready code, navigating technical trade-offs to ensure system reliability. - Develop a deep understanding of incentives, pricing, and marketplace dynamics to build systems that align with operational needs and business goals. - Contribute to high engineering standar