Who can apply?
- Uber lists its US and Canada Internship and Co-Op Programs as open to undergraduate and graduate students across all teams and locations, with 12-week internships and 12-to-24-week co-ops.
- For data science internships, Uber says its Data Scientist roles look for current bachelor's or master's students in analytical fields, and that all candidates should have at least one semester or quarter of education left after the internship.
- The overview does not state pay, a GPA or work-authorization conditions; individual postings carry those.
The one-term-remaining rule stated for data science internships means a student who finishes their degree before or during the summer does not qualify, even with the right major.
Dates, pay & program timing
Uber's emerging-talent page gives September to November as the time to apply for internships and co-ops, and says data science internship applications open in the fall for the following summer. Data science internships last 12 weeks in San Francisco, Sunnyvale, Seattle or New York. The page describes a recurring pattern and names no specific deadline.
View the deadline calendarThese details reflect the sources at the review date. An overview is not a live job offer. Check the official page for today's application status and the selected opening's written terms.
How to apply to Uber
- Open Uber's emerging-talent page, select the US&C region, and choose between the general internship and co-op route and the data science and applied science track.
- From September to November, search Uber's job site for current university roles and read each posting for pay, location and requirements.
- Apply to the specific posting and save a copy of its description, since postings come down once filled.
You leave FirstInternships to apply. We do not accept applications, offer referrals, or represent the employer.
Our editorial advice · not employer requirements
How to prepare a stronger application
Uber runs a physical marketplace — riders, drivers, couriers and merchants — so a project that handled two sides of a system, matching, pricing or real-time location data is unusually relevant. Explain the constraint that made it hard, not just the tools.
For data science roles, prepare an analysis where you had to define the metric yourself. Being able to say why you chose it, and what it would miss, shows the judgment such roles depend on. This is our suggestion, not Uber's stated process.
Your preparation, on this device
Make it a plan.
Checking an item saves this program. These are planning prompts, not a substitute for the employer's full requirements.
Add a stage & next actionOfficial sources & update notes
We reviewed these publisher pages on . They establish the program facts above; our preparation suggestions are independent editorial guidance.
We do not infer deadlines, remote eligibility, or guaranteed offers from older recruiting cycles. Read our editorial process.