2026 PhD Applied Scientist Intern

We’re looking for Ph.D. students specializing in Machine Learning and Generative AI to intern during Summer 2026 (12 weeks). As a Ph.D. intern, you will be embedded in a product team working on solving real-world Uber problems and will have the opportunity to partner closely with other Applied Research Scientists, Software Engineers, Product Managers, and other cross functional partners.

About the Team

The Trusted Identity Applied Science team builds ML models and GenAI solutions to detect and mitigate identity fraud on Uber platform and across all LoBs. Part of Uber Core Services organization, the team is focused on building large-scale modeling solutions to make sure only legitimate, verified and authorized users can access Uber products and services. As a member of a concentrated team of ML model developers, you will play an influential role in building solutions in a highly cross-functional and collaborative environment and help make our platform as safe as possible for all users.

What You’ll Do

• Work with a mentor closely to onboard and get familiarity with Uber internal tools and modeling frameworks
• Dep dive into a business problem, scope a model development project, develop, and prototype the solution
• Evaluate impact and present findings to leaders and cross-functional stakeholders
• Support statistical and machine learning efforts including deep learning, generative AI, NLPs, signal processing, time series analysis, and more

Basic Qualifications

• Current Ph.D. student majoring in Computer Science, Statistics, Machine Learning, Electrical Engineering, Operations Research, Mathematics,, or other related quantitative fields
• Candidates should have at least one semester/quarter left of their education after finishing the internship
• Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, and generative AI
• Strong problem solving and analytical abilities
• Strong hands-on experience with scripting languages and tools such as Python/R
• Familiarity with SQL
• Familiarity with programming languages such as GoLang and Java

Preferred Qualifications

• Ability to communicate effectively with both technical and business partners
• Research mentality with a bias towards action to structure a project from idea to prototype to productionization
• Independent and coachable contributor with well communication skills, and outstanding follow-through and ownership mindset - you energetically tackle your work and love the responsibility of being individually empowered
• Ability to deep dives and case studies to understand the complex underlying problem, perform exploratory data analysis, statistical analysis, model development, prototyping, and coding

For San Francisco, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour.

For Sunnyvale, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour.

For all US locations, you will also be eligible for various 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.