We're looking for a Lead, Applied AI to architect and scale intelligent automation solutions that transform how Uber Advertising's Measurement Science function operates. You'll lead the technical strategy for AI-driven workflows, build production-grade AI systems, and partner with cross-functional leadership across Measurement Science to identify high-impact automation opportunities. This position is designed for a technical builder who excels in a high-code environment and is passionate about organizational enablement. You will manage the core technical infrastructure required for secure and reliable AI operations while simultaneously spearheading the integration and adoption of these advanced capabilities throughout the Measurement Science organization.

Build production AI applications end to end that solve concrete measurement problems for our partner teams, taking systems from idea to launch; Design and implement AI agents and agentic workflows using frameworks like LangChain, LangGraph, or MCP; Develop RAG systems including vector store implementation, embedding strategy, retrieval logic, and context management; Apply foundation models and LLMs through prompt engineering, orchestration, and lightweight fine-tuning where needed (foundational model development continues to live with our AI/ML pod); Architect AI-specific data access patterns and APIs that allow agents to query data safely (core data pipelines remain owned by our Data Infrastructure pod); Build AI-powered internal tools, conversational interfaces, and Slack apps that drive adoption across the org; Establish AI safety and reliability practices including guardrails, evaluation, fallback logic, and PII protections; Build evaluation infrastructure to measure AI output quality, catch regressions, and continuously improve performance; Champion engineering excellence across testing, observability, and incident response for AI systems; Partner with cross-functional leadership to spot high impact AI opportunities and translate business problems into AI solutions; Drive adoption through documentation, partnership with end users, and ongoing iteration based on feedback; Set technical direction for AI across our team by establishing patterns and reusable components other engineers can build on