Job Description
About Us
We're looking for Machine Learning PhD candidates to intern during summer 2025 (12 weeks). You will be embedded in the engineering team behind Uber's Michelangelo Machine Learning and work closely with other engineers, data scientists, and product managers. As a PhD intern, you will work on an exciting yet bold problem in depth independently, under the supervision of an experienced engineer on that team.
Our team owns and maintains ML Platform that powers hundreds of ML models in production and serves more than 1 million predictions per second. For the upcoming year we want to improve our tooling for both model explainability and recommender systems, both of which require some novel research.
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To succeed in this position, you will need to have a strong AI/ML background with a focus on model explainability and/or recommender systems.
What You'll Do
- Drive exciting, ambitious, previously unsolved projects from end to end.
- Thrive in ambiguous product requirements.
- Iterate fast to explore possible solutions.
- Make data-driven decisions with exceptional execution.
- Collaborate closely with product managers and data scientists.
- Be motivated to own projects and push them forward with independence.
Basic Qualifications
- Ongoing PhD in Computer Science or Machine Learning.
- Great understanding of both classical and modern AI/ML techniques.
- Excellent coding skills and software design skills.
- Track record of published papers.
Preferred Qualifications
- Experience with Recommender systems, ideally with continuous retraining
- Experience with model explainability
- Ability to communicate effectively with both technical and business partners.
- Experience in simplifying/converting business problems into technical problems.
- Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation.
- Experience presenting at industry-recognized academic conferences.
At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 10,000 cities around the world.