Software Engineering Intern (PhD Year 3+)

Description du poste:

We're looking for machine learning engineers who are currently completing or recently completed a PhD program and who are passionate about building high-impact, consumer-facing products. In this role, you'll work across the full end-to-end flow of Uber's Delivery products-from building machine learning models and offline data pipelines, to developing real-time services, online model serving, and product user experiences. You'll have the opportunity to tackle challenging problems at scale, shape core pricing mechanisms, and drive impactful outcomes across the entire consumer journey.

About The Team

The Consumer Structural Pricing team plays a pivotal role in shaping consumer demand across Uber's Delivery business-including food delivery, groceries, and more. We work closely with other Uber Marketplace teams to build innovative products and scalable systems that keep the marketplace efficient, reliable, and ready for continued growth. Our systems power hundreds of millions of consumers and millions of merchants around the world, and that footprint is expanding rapidly.

What You'll Do

* Design and build innovative products used by hundreds of millions of consumers, in collaboration with talented engineers, Product Managers, Product Operations, and Applied/Data Scientists
* Develop and optimize ML models to enhance the efficiency of key delivery marketplace pricing levers
* Build offline data pipelines using Hive or similar technologies
* Write clean, maintainable, and high-quality code

Basic Qualifications

* Completing or recently completed a PhD in Statistics, Mathematics, Computer Science, Machine Learning, or related quantitative field

Preferred Qualifications

* Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods
* Strong sense of ownership and accountability
* Strong problem-solving skills, with expertise in ML methodologies
* Ability to thrive in a fast-paced, collaborative, and team-oriented environment