Staff ML Engineer, Generative AI

Uber Uber · Consumer · Sunnyvale, CA · Engineering

Staff ML Engineer to architect, productionize, and scale an autonomous support agent that resolves customer issues end-to-end, focusing on LLM orchestration, evaluation, safety guardrails, and cost efficiency.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Mentor/principle‑lead multiple pods; set technical strategy and quality bars; coach senior engineers on agentic patterns, reliability, and experiment velocity.

Skills

Required

  • LLM-driven systems
  • inference optimization
  • prompt/program design
  • fine-tuning
  • distillation/LoRA
  • safety/guardrails
  • evals
  • customer-facing intelligent experiences
  • A/B testing
  • metrics literacy

Nice to have

  • agentic architectures
  • planner/executor
  • memory
  • multi-step reasoning
  • RAG
  • support automation
  • routing
  • policy codification
  • internal tooling
  • co-pilot/auto-resolve
  • Multilingual NLU/NLG
  • code-switching
  • low-resource languages
  • hallucination mitigation
  • safety red-teaming
  • privacy-by-design
  • speed
  • reliability
  • experiment frameworks
  • feature flags
  • canary/guarded rollouts
  • kill-switches

What the JD emphasized

  • 7+ years building production ML/AI systems
  • 2+ years leading complex ML initiatives end‑to‑end
  • customer-facing
  • cost per contact
  • Agentic architectures
  • RAG

Other signals

  • autonomous support agent
  • LLM orchestration
  • agentic architectures
  • customer-facing intelligent experiences