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Staff Machine Learning Engineer, Core Services Eng (GenAI)

Machine Learning, Engineering
San Francisco, California |
Sunnyvale, California
Full Time

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 you will 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.

Basic Qualifications

  1. 7+ years building production ML/AI systems; 2+ years leading complex ML initiatives end‑to‑end.
  2. Deep expertise in LLM‑driven systems (inference optimization, prompt/program design, fine‑tuning, distillation/LoRA, safety/guardrails, evals).
  3. Track record of shipping customer‑facing intelligent experiences with measurable impact (A/B testing, metrics literacy).
  4. Bachelor's Degree, or above, in Comp Science or related field.

Preferred Qualifications

  1. Agentic architectures in production (planner/executor, memory, multi‑step reasoning) and RAG over complex, policy‑heavy knowledge bases.
  2. Experience building support automation for large consumer platforms (routing, policy codification, internal tooling, co‑pilot/auto‑resolve).
  3. Multilingual NLU/NLG (code‑switching, low‑resource languages), hallucination mitigation, safety red‑teaming, and privacy‑by‑design.
  4. Practical expertise balancing speed and reliability at scale: experiment frameworks, feature flags, canary/guarded rollouts, and clear kill‑switches.

For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.


See our Candidate Privacy Statement

Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, Veteran Status, or any other characteristic protected by law.

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ご希望の言語を選択してください

アラビア語, العربيةアッサム語, অসমীয়াアゼルバイジャン語, Azərbaycancaブルガリア語, Българскиベンガル語, বাংলাカタロニア語 (スペイン), Català (Espanya)チェコ語, Češtinaデンマーク語, Danskドイツ語, Deutschギリシャ語, Ελληνικά英語, Englishスペイン語, Español (Internacional)スペイン語, Español (Argentina)スペイン語, Español (Chile)スペイン語, Español (Colombia)スペイン語, Español (Costa Rica)スペイン語 (イベリア半島), Castellanoスペイン語, Español (Honduras)スペイン語, Español (México)スペイン語, Español (Uruguay)エストニア語, Eestiフィンランド語, Suomiフランス語 (カナダ), Français (Canada)フランス語, Français (France)ヘブライ語, עבריתヒンディー語, हिन्दीクロアチア語, Hrvatskiハンガリー語, Magyarインドネシア語, Bahasa Indonesiaイタリア語, Italiano日本語, 日本語ジョージア語, ქართულიカンナダ語, ಕನ್ನಡ韓国語, 한국어クルド語, کوردیリトアニア語, Lietuviųラトビア語, Latviešuマラヤーラム語, മലയാളംマラーティー語, मराठीノルウェー語(ブークモール), Norsk Bokmålネパール語, नेपालीオランダ語, Nederlandsパンジャブ語, ਪੰਜਾਬੀポーランド語, Polskiポルトガル語 (ブラジル), Português (Brasil)ポルトガル語 (イベリア半島), Português (Portugal)ルーマニア語, Românăロシア語, Русскийシンハラ語 (スリランカ), සිංහලスロバキア語, Slovenčinaスロベニア語 (スロベニア), Slovenščinaスウェーデン語, Svenskaスワヒリ語, Kiswahiliタミル語, தமிழ்テルグ語, తెలుగుタイ語, ไทยトルコ語, Türkçeウクライナ語, Українськаウルドゥー語, اردوベトナム語, Tiếng Việt中国語, 简体中文中国語 (中華人民共和国香港特別行政区), 香港中文中国語 (台湾), 繁體中文