Staff Machine Learning Engineer - Ads

Staff Machine Learning Engineer - Ads Department: Engineering Team: Machine Learning Location: New York, United States Type: Full-Time The Ads Machine Learning team at Uber is responsible for designing, building, and evolving the core ML systems that powers ads selection, ranking, pricing, and delivery across the Uber ecosystem. We develop a deep understanding of user intent and merchant objectives to produce high quality ML signals that drive large scale auction based decision making. These systems operate under strict latency, reliability, and fairness constraints while serving billions of predictions that directly impact user experience, advertiser performance, and revenue outcomes. As a Staff Machine Learning Engineer, you will play a central role in defining and executing the Ads ML technical roadmap. You will lead the design of next generation recommendation and auction architectures, enable step function improvements in model quality and serving efficiency, and raise the bar on observability and reliability of online ML systems. This role requires end to end ownership across modeling, training, online inference, and system integration, as well as close collaboration with product, infrastructure, and platform teams. Delivering robust, scalable, and measurable ad recommendations is critical to Uber’s rapidly growing Ads business, making this a highly visible and high impact role. **What the Candidate Will Do:** - Lead the design and evolution of machine learning models that power ads ranking, pricing, and auction systems at scale. - Own end to end ML systems, including training pipelines, feature infrastructure, and low latency online inference for real time and batch use cases. - Apply advanced statistical and ML techniques to improve ads relevance, marketplace efficiency, and advertiser outcomes. - Define experimentation strategies, success metrics, and evaluation frameworks, and drive iteration through rigorous offline and online testing. - Establish model and system observability through metrics, dashboards, and reliability best practices. - Translate ambiguous product goals into durabl