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Staff ML Engineer - GenAI

Machine Learning, Engineering
in Hyderabad, India

About the Role

We are looking for a highly motivated GenAI Engineer to join the Customer Obsession team. You will play a critical role in designing conversational GenAI systems and algorithms which would enhance the customer support experience and resolution speed for millions of Uber Eats users worldwide while making O(100s millions) cost savings. You will leverage your expertise in data analysis, machine learning, and engineering to drive insights, identify tech-driven product innovations, optimize algorithms and systems ultimately improving user satisfaction and operational efficiency.

What the Candidate Will Do:

  1. Design, develop, and productionize Conversational GenAI solutions in the field of customer support engineering spanning generative AI algorithms, agentic AI design at scale, NLP for query understanding and ranking responses, distillation techniques, etc.
  2. Productionize and deploy these models for real-world applications in customer support.
  3. Design and analyze experiments using a combination of data analysis/statistical analysis to lead the team to a reasonable inference.
  4. Review code and designs of teammates, providing constructive feedback.
  5. Collaborate with cross-functional teams to brainstorm new solutions and iterate on the product.
  6. Technically lead the team, mentor and guide engineers

What the Candidate Will Need:

  1. Experience in building and owning Conversational GenAI models over multiple years, including strong understanding of product and operational metrics and what it takes to improve them.
  2. Bachelor's or Master's in Computer Science, Statistics, or a related field or Equivalent Experience in Conversational GenAI
  3. Minimum 10+ years of experience in industry with a strong focus on machine learning and optimization.
  4. Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
  5. Solid understanding of statistical analysis and feature engineering techniques.
  6. Excellent communication and collaboration skills.
  7. Ability to work independently and take ownership of projects.
  8. Experience using SQL in a production environment.
  9. Experience in experimental design and analysis, exploratory data analysis, and statistical analysis.
  10. Experience with dashboarding and using data visualization tools.
  11. Experience using statistical methodologies such as sampling, statistical estimates, descriptive statistics, or similar.

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 fuelds progress. What moves us, moves the world - let’s move it forward, together.

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.

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.


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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선호하는 언어 선택

아랍어, العربية아삼어, অসমীয়া아제르바이잔어, Azərbaycanca불가리아어, Български벵골어, বাংলা카탈로니아어(스페인), Català (Espanya)체코어, Čeština덴마크어, Dansk독일어, Deutsch그리스어, Ελληνικά영어, English스페인어, Español (Internacional)스페인어, Español (Argentina)스페인어, Español (Chile)스페인어, Español (Colombia)스페인어, Español (Costa Rica)스페인어(유럽), Castellano스페인어, Español (Honduras)스페인어, Español (México)스페인어, Español (Uruguay)에스토니아어, Eesti핀란드어, Suomi프랑스어(캐나다), Français (Canada)프랑스어, Français (France)히브리어, עברית힌디어, हिन्दी크로아티아어, Hrvatski헝가리어, Magyar인도네시아어, Bahasa Indonesia이탈리아어, Italiano일본어, 日本語조지아어, ქართული칸나다어, ಕನ್ನಡ한국어, 한국어쿠르드어, کوردی리투아니아어, Lietuvių라트비아어, Latviešu말라얄람어, മലയാളം마라티어, मराठी노르웨이어(보크말), Norsk Bokmål네팔어, नेपाली네덜란드어, Nederlands펀잡어, ਪੰਜਾਬੀ폴란드어, Polski포르투갈어(브라질), Português (Brasil)포르투갈어(유럽), Português (Portugal)루마니아어, Română러시아어, Русский싱할라어(스리랑카), සිංහල슬로바키아어, Slovenčina슬로베니아어(슬로베니아), Slovenščina스웨덴어, Svenska스와힐리어, Kiswahili타밀어, தமிழ்텔루구어, తెలుగు태국어, ไทย터키어, Türkçe우크라이나어, Українська우르두어, اردو베트남어, Tiếng Việt중국어, 简体中文중국어(홍콩[중국 특별행정구]), 香港中文중국어(대만), 繁體中文