Job Description
About the role
Uber Engineering Internship is an outstanding opportunity for PhD students who are passionate about technology to work alongside our community of engineers and bridge the gap between academic theoretical knowledge and practical professional experience.
Uber Engineering is growing quickly as we look to take on exciting opportunities at scale around the world. As we grow our team and systems, there is a growing need for automatic program analysis tools to keep our code quality and systems performance high while ensuring our engineers can continue to adapt quickly. To support this need, the Programming Systems Group is looking for doctoral interns to innovate and develop new optimizations, static and dynamic analysis tools, programming models, and Generative AI techniques, and apply them to Uber's code base and production systems.
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The team aims to accelerate performance, reliability, and developer productivity across a variety of languages, including Go, Java (mobile and backend), Swift, Kotlin, and TypeScript. The research includes a mix of exploration, development, and extensions of core infrastructure to address key, previously unsolved issues. The team's recent work includes static analysis tools such as NullAway, NilAway, and Piranha; dynamic analysis tools such as CRISP, datarace detection, and leakprof ; optimization tools such as swift protocol specialization, iOS binary size optimization, and profile-guided optimization in Go; and Generative-AI tools for fixing concurrency bugs. We make our tools open source and publish our research in blog posts and academic papers. We also participate actively in the research community, including attending conferences, organizing workshops, and serving on program committees.
To succeed in this position, you will have a solid understanding of innovative program analysis techniques and experience in developing static analyses or compiler optimizations. You should also be passionate about writing clean, efficient, and well-tested code.
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.
- Most importantly, have a passion for making Uber better for our customers.
- Publish your work at top computer science conferences.
Basic Qualifications
- Prior experience with compiler optimization, static or dynamic analysis, and Generative AI.
- Solid understanding of modern program analysis techniques.
- Excellent coding skills and software design skills.
- Published papers in program analysis or code optimization.
Preferred Qualifications
- 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.
To be eligible for this internship opportunity, we require you to be:
- Currently enrolled in a PhD program in Computer Science, Computer Engineering, or related field.
- Ability to work on-site in the Netherlands (Amsterdam) or Denmark (Aarhus)
- Available for a 3-month, full time internship in the summer 2025.
Perks/Benefits
- A competititve monthly internship allowance along with housing support
- The rare opportunity to change the world such that everyone around you is using the product you built - we're moving real people and assets and reinventing transportation and logistics globally
- Receive mentorship from Uber engineers to guide you through your summer experience
- Build personal networks and friendships with hardworking people who share your passion for technology in a lively international work environment
- The possibility of joining the team permanently in the future