Staff ML Engineer, Generative AI

Uber

Hybrid

Quick summary

Work type
Hybrid
Location
Sunnyvale, CA
Salary
$232,000–$232,000 / yr
Posted
12 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $217k
This role $232k
$170k most similar roles pay here $270k

This role pays more than 61% of similar roles. Most pay $187,296–$246,150 — the shaded band above. At the midpoint, this role pays about $232k versus about $217k for comparable roles.

Based on 240 similar postings.

Employer

About Uber

Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.

Uber currently has 95 open roles on FindRole.

Listed pay typically runs $232,000–$232,000 across 76 roles with salary data.

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At a glance

TL;DR · Staff ML Engineer, Generative AI

As a Staff ML Engineer on Uber’s Customer Obsession team in Sunnyvale, you will architect, productionize, and scale an autonomous support agent that resolves customer issues across mobile, web, and voice platforms at global scale. Your responsibilities include owning the end-to-end agent architecture, advancing retrieval and reasoning pipelines, establishing evaluation frameworks for safety and efficiency, driving automation to reduce cost per contact, and mentoring senior engineers. You will work with LLM-driven systems, including inference optimization and prompt design, while ensuring compliance and reliability in a multilingual environment. The role requires deep expertise in agentic architectures, RAG over complex knowledge bases, and support automation for large consumer platforms. Ideal candidates have a track record of shipping customer-facing intelligent experiences and balancing speed and reliability at scale.

What you'll do

  • Own the end-to-end architecture of an autonomous support agent for customer issues.
  • Advance retrieval and reasoning pipelines to ensure consistent and policy-driven responses.
  • Establish evaluation systems for LLMs including safety tests and cost efficiency metrics.
  • Drive automation strategies to scale support while reducing cost per contact.
  • Mentor senior engineers on technical strategy, reliability, and experiment velocity.

What we're looking for

  • 7+ years of experience building production ML/AI systems and leading complex initiatives.
  • Deep expertise in LLM-driven systems including inference optimization and safety guardrails.
  • Track record of shipping customer-facing intelligent experiences with measurable impact.
  • Experience with agentic architectures and RAG over policy-heavy knowledge bases.
  • Practical skills in balancing speed and reliability at scale for large consumer platforms.

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