Staff Software Engineer AI Data

About the Role

Uber's next generation of AI is built upon its real-time nervous system-a platform that processes billions of critical messages for everything from ETAs to driver-rider communications. Our team's mission is to transform this vast stream of data into the core knowledge that powers intelligent, autonomous agents across the business.

We're seeking a foundational Staff Machine Learning Engineer to pioneer Uber's next generation of AI. As the key technical leader, you will architect and build a self-evolving platform from the ground up, translating vast, real-time data into a central knowledge base to power intelligent, autonomous agents. This role is for a visionary engineer who thrives in ambiguous, "0-to-1" environments and is passionate about building novel, large-scale AI systems with a direct global impact.

- What the Candidate Will Do ----

1. Architect the Platform: Lead the technical design of the end-to-end system for data ingestion, knowledge extraction, and action generation.
2. Build the "Data to Knowledge" Pipeline: Implement robust, scalable systems for generating contextual embeddings and constructing a dynamic knowledge graph from diverse, real-time data sources.
3. Develop Self-Evolving Systems: Design and integrate online learning and reinforcement learning models to ensure the platform continuously adapts and improves.
4. Drive Technical Decisions: Own the critical decisions on frameworks, models, and infrastructure needed to bring ideas to production.

1. System Building Experience: 8+ years of experience designing, building, and shipping production AI/ML systems at scale. Specific experience with agentic systems, search/recommendation platforms, or data-intensive applications is highly desirable.
2. LLM & Foundation Models: Deep hands-on experience with Large Language Models. This includes fine-tuning, retrieval-augmented generation (RAG), Context Engineering, Prompt Engineering, and deploying models for real-world applications.
3. Agent & Tooling Expertise: Proven experience in building systems that enable AI agents to reason, plan, and use external tools (APIs, databases).
4. Technical Proficiency: Expert-level proficiency in Python and technologies for large-scale data processing (e.g., Spark, Flink, Ray).

1. Familiarity with building ML platforms or infrastructure.
2. Direct experience with knowledge graphs.
3. Contributions to open-source AI/ML projects or publications in relevant fields.
4. Experience with reinforcement learning or online learning systems.