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 ROLE** Applied AI at Uber builds intelligent systems that power next-generation product experiences for riders, drivers, merchants, and couriers. As a Staff AI/ML Engineer, you will operate as a full-stack technical leader across AI, backend infrastructure, and machine learning platforms — owning systems end-to-end from model development to highly reliable, large-scale distributed services that power real-time AI experiences in production. This role requires strong AI/ML and infrastructure expertise, including designing and operating distributed systems, ML platforms, and data-intensive services, alongside hands-on development of machine learning and generative AI models. You will build and scale production-grade ML infrastructure, enable rapid experimentation, and ensure reliability, observability, and cost efficiency at Uber scale. You will partner closely with platform, infra, and product teams to define foundational AI services, establish ML system abstractions, and set architectural direction for how AI capabilities are built, deployed, and operated across Uber’s ecosystem. This role is ideal for engineers who operate comfortably as both AI experts and backend infrastructure leaders, setting technical direction and raising the bar for production ML systems at scale. ##### **WHAT YOU’LL DO:** - **Build end-to-end AI products** — from prototype to scalable production deployment — integrating LLMs and multimodal AI into Uber’s consumer, earner, and enterprise experiences. - **Implement automated evaluation systems** that use LLM-as-a-judge techniques to benchmark model quality, ensure consistency, and accelerate experimentation. - **Design and implement** **high-throughput, low-latency backend services and APIs** that connect to leading AI models (e.g., OpenAI, Claude, Gemini, Mistral), ensuring production reliability, low latency, fault tolerance, and cost optimization at scale. - **Lead the development of ML infrastructure** for training,