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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.
Job Responsibility:
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
Architect AI-specific data access patterns and APIs that allow agents to query data safely
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
Requirements:
5+ years of hands-on engineering experience, with meaningful time spent building AI or automation systems
Track record of shipping production AI or automation applications that drove measurable business impact
Advanced Python and SQL proficiency for working with data at scale
Production experience integrating LLMs into real applications, including prompt engineering, retrieval, and evaluation
Experience designing and consuming APIs, with comfort building reliable services (FastAPI, Flask, or similar)
Working knowledge of at least one AI orchestration framework such as LangChain, LangGraph, or MCP
Familiarity with vector databases and RAG patterns
Solid engineering fundamentals around Git, CI/CD, testing, and event driven systems
Strong cross-functional communication skills and the ability to drive adoption of new tools across non technical teams
Nice to have:
Experience with AI evaluation tools and observability for non deterministic systems
Built Slack apps, conversational interfaces, or other AI-powered internal tools with strong adoption
Hands-on experience designing agentic systems with function calling, tool use, or multi agent orchestration
Production cloud experience (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
Integration experience with business platforms like Salesforce, Slack, Google Workspace, or Jira
Background in ad tech, measurement, or analytics platforms