Staff Machine Learning Engineer - Applied AI

Staff Machine Learning Engineer - Applied AI Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the Team:** The Applied AI team collaborates with product teams across Uber to deliver innovative AI solutions for core business problems. We work closely with engineering, product and data science teams to understand core business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Personalization, Generative AI, Computer Vision, ML Optimization and Geospatial AI. **About the Role:** We are building AI-native discovery experiences across Mobility and Delivery. Search, recommendations, and conversational AI are central to how millions of users discover rides, restaurants, grocery items, and retail products every day.  We are hiring a Staff ML Engineer (IC6) to define and lead the foundation model strategy powering these experiences. At this level, you will not just build models — you will shape technical direction across teams, influence product strategy, and deliver measurable impact at global scale. **What the Candidate Will Do** - Own the end-to-end technical strategy for foundation models across Search, Recommendations, and Conversational AI. - Drive architecture decisions that influence multiple product surfaces (Eats, Grocery, Retail, Mobility). - Lead cross-team initiatives spanning Retrieval, Ranking, Personalization, and LLM-powered assistants. - Define long-term investment areas (build vs fine-tune vs partner models). - Mentor senior engineers and act as a technical multiplier across the org. **Basic Qualifications** - Masters degree or Ph.D in Computer Science, Engineering, Mathematics - 8+ years of ML experience, including significant work on large-scale deep learning systems. - Demonstrated ownership of high-impact ML systems in search, recommendations, or conversational AI. - Deep expertise in transformers, retrieval systems, ranking, and embedding architectures. - Strong experience with PyTorch and distributed training . - Track record of influencing technical directio