Staff ML Engineer, Generative AI
- Compensation
- Not disclosed
- Location
- Sunnyvale, CA, United States
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Full time
- Experience
- 2+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 7+ years building production ML/AI systems; 2+ years leading complex ML initiatives end‑to‑end.
- Deep expertise in LLM‑driven systems (inference optimization, prompt/program design, fine‑tuning, distillation/LoRA, safety/guardrails, evals).
- Track record of shipping customer‑facing intelligent experiences with measurable impact (A/B testing, metrics literacy).
- Bachelor's Degree, or above, in Comp Science or related field.
Nice to have
- Agentic architectures in production (planner/executor, memory, multi‑step reasoning) and RAG over complex, policy‑heavy knowledge bases.
- Experience building support automation for large consumer platforms (routing, policy codification, internal tooling, co‑pilot/auto‑resolve).
- Multilingual NLU/NLG (code‑switching, low‑resource languages), hallucination mitigation, safety red‑teaming, and privacy‑by‑design.
- Practical expertise balancing speed and reliability at scale: experiment frameworks, feature flags, canary/guarded rollouts, and clear kill‑switches.
Full description
About the Role
Uber’s Customer Obsession team builds the platform and AI that powers world‑class support across mobile, web, and voice at global scale. We are now hiring a Staff ML Engineer to architect, productionize, and scale an autonomous support agent that resolves customer issues end‑to‑end. Experience with agentic architectures is a major plus. You’ll push the state of the art in GenAI for customer service—LLM orchestration, evaluation, safety guardrails, multilingual support—while holding a very high bar for reliability and cost efficiency. We are still at an early stage and value candidates with bias for action who get creative with GenAI tools to accelerate execution and experimentation.
What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
- Own the end‑to‑end agent architecture: agentic planning and execution loops, long-term memory, persona/voice, knowledge routing, and policy enforcement for compliant, on‑brand conversations.
- Advance retrieval & reasoning: Build next-generation retrieval and reasoning pipelines, where the agent can search across different knowledge sources, apply policy-driven tools, and call structured workflows and ensure that responses are consistently grounded.
- Establish evals that matter: offline rubrics, simulated scenarios, safety tests, cost/latency tradeoff suites, and LLM‑as‑judge (with calibrated human review) wired into CI/CD and experiment platforms.
- Drive automation at scale: partner with Product/Design/Operations on coverage, policy alignment, localization, and rollout strategy to better customer experience and reduce cost per contact.
- Mentor/principle‑lead multiple pods; set technical strategy and quality bars; coach senior engineers on agentic patterns, reliability, and experiment velocity.
---- Basic Qualifications ----
- 7+ years building production ML/AI systems; 2+ years leading complex ML initiatives end‑to‑end.
- Deep expertise in LLM‑driven systems (inference optimization, prompt/program design, fine‑tuning, distillation/LoRA, safety/guardrails, evals).
- Track record of shipping customer‑facing intelligent experiences with measurable impact (A/B testing, metrics literacy).
- Bachelor's Degree, or above, in Comp Science or related field.
---- Preferred Qualifications ----
- Agentic architectures in production (planner/executor, memory, multi‑step reasoning) and over complex, policy‑heavy knowledge bases.