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2026 PhD Software Engineer Intern (Delivery Marketplace), United States

Uber

2026 PhD Software Engineer Intern (Delivery Marketplace), United States

Uber

·

On-site

·

Internship

·

4d ago

We're looking for PhD candidates in the machine learning and optimization domain to intern with our Delivery Marketplace team during summer 2026 (12 weeks). You will be embedded in an engineering team and work closely with other specialists, data scientists, and product managers. As a PhD intern, you will work on an exciting yet bold problem independently, under the supervision of an experienced engineer on that team.

About the Role:

At Uber, we work on many ambitious engineering products covering many lines of business as well as the underlying platform technologies that power those businesses. We foster growth and increase profitability of Uber by pushing the frontiers of machine learning, constrained optimization, statistics, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry.

As a PhD software engineer intern, you will have a lot of opportunities to work with product managers, data scientists and, of course, engineers from different teams. You will have an opportunity to learn how to iterate over a product for greater success while demonstrating your area of expertise (machine learning, statistics, constrained optimization, distributed system, etc.). This is a unique opportunity to grow your skills with real-world experience and do highly impactful, yet fun work at the same time. It is an ambitious yet rewarding job!

About the Team:

The Delivery Marketplace: Consumer and Courier Pricing, Matching and Logistics is responsible for building the core technology across the delivery line of business including food delivery, grocery, and retail last mile. These include a diverse set of transformational projects and impactful products ranging from finding the optimal match between jobs and earners in the delivery space, to setting optimal prices for Eaters and Couriers. The organization is formed of talented teams of engineers and scientists working side by side on the state of the art innovations on the matching objective function, pricing algorithms, and promotions. To achieve these goals, we leverage big data tools and apply advanced machine learning models to drive top line and bottom line business outcomes.

  • What You'll Do

  • Drive exciting, ambitious, previously unsolved projects from end to end

  • Develop novel algorithms that use Uber data at global scale

  • Thrive in an environment with ambiguous product requirements

  • Collaborate closely with product managers and data scientists

  • Be motivated to independently own and drive projects forward

  • Have a passion to make Uber better for our customers

  • Basic Qualifications

  • Currently enrolled in a Ph.D. program studying computer science, machine learning, data mining, artificial intelligence, constrained optimization, statistics, or a related quantitative field

  • Candidates must have at least one semester/quarter of their education left following the internship

Preferred Qualifications:

  • Knowledge of underlying technical foundations of statistics, machine learning, optimization, systems, etc.
  • Experience in one or more object-oriented languages, including C++, Java, Python, or Go
  • 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

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

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.

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About Uber

Uber

Uber develops, markets, and operates a ride-sharing mobile application that allows consumers to submit a trip request.

10,001+

Employees

San Francisco

Headquarters

$120B

Valuation

Reviews

3.1

10 reviews

Work Life Balance

4.2

Compensation

2.3

Culture

3.5

Career

2.0

Management

2.5

45%

Recommend to a Friend

Pros

Flexible hours and schedule

Meeting different people and cultures

Make your own hours

Cons

Inconsistent and low pay

Safety concerns with passengers

Traffic and difficult drivers

Salary Ranges

23,534 data points

Mid/L4

Mid/L4 · Data Analyst

3 reports

$209,300

total / year

Base

$161,000

Stock

-

Bonus

-

$203,580

$209,300

Interview Experience

5 interviews

Difficulty

3.0

/ 5

Duration

14-28 weeks

Offer Rate

40%

Experience

Positive 80%

Neutral 20%

Negative 0%

Interview Process

1

Application Review

2

Online Assessment

3

Recruiter Screen

4

Technical Phone Screen

5

Case Study/Analytics Test

6

Final Loop/Panel Interview

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Case Study

Technical Knowledge