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2 Days ago

Machine Learning Engineer II

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Information Technology
Full-Time
Uber

Overview

Uber is proud to be an Equal Opportunity/Affirmative Action 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.

What The Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----

  • Build GenAI powered customer support assistants through prompt engineering 3P LLMs or fine-tuning custom LLMs.
  • Work with stakeholders across engineering, product and data science to understand product requirements, prototype, iterate and launch GenAI assistants in production.
  • Explore novel ideas and innovative solutions that can lead to a step change in our products

Basic Qualifications

  • Master's degree in Computer Science or closely related field.
  • Knowledge of latest ML algorithms such as GenAI, NLP, CV, recommendation systems.
  • Coding chops, clean, elegant, bug-free code in languages like Java, GO
  • Strong desire to learn and grow, while building the best-in-class systems

Preferred Qualifications

  • Deep knowledge of NLP theory and an understanding of the latest innovations in Generative AI
  • Contributed to the broader technical community through tech talks, publications, open source projects, or other ways.
  • Have a proven track record working across an organization not just across teams.
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