# Research Intern at Build AI

- Company: [Build AI](https://build.ai)
- Location: San Francisco, CA
- Type: Internship · Internship
- Posted: 2026-08-30
- Apply: https://jobs.ashbyhq.com/build-ai/14e404c4-e189-4e49-9ef7-238da9655caf
- Page: https://newgrad.ai/jobs/research-intern-44e1aa52

## Summary by newgrad.ai

What you'll do:

- Execute a 12-week research project using physical labor datasets.
- Train, evaluate, and ablate models while building necessary data pipelines.
- Collaborate with researchers to improve data collection and evaluation methods.

What they're looking for:

- Students pursuing a Bachelor’s, Master’s, or PhD in technical fields.
- Experience training models using PyTorch or similar frameworks.
- Ability to work independently in person at the San Francisco office.
- Background in video, world models, robotics, or large-scale training.

Pay and perks:

- Competitive salary plus medical, dental, and vision insurance.
- Monthly $2,000 housing subsidy for those living near the office.
- Daily office meals, relocation support, and unlimited compute budget.

## Posting

### ABOUT BUILD AI

Build AI is the data hyperscaler for Physical AI. We're vertically integrated across hardware, manufacturing, logistics, collection, and model training to scale the physical labor dataset orders of magnitude faster than anyone in the world.

### JOB SUMMARY

We’re hiring Research Interns to train on the physical labor dataset. You run a real research bet for about 12 weeks, not a toy project. Collection is monocular 1920×1080p 30fps, targeting 100M hours. If you have a bet on video, world models, or physical-labor data, you might as well run it here.

### KEY RESPONSIBILITIES

- Run a research bet on Build data: train, evaluate, ablate, and write down what the data actually taught
- Work with researchers, dataset, and evals so the internship changes collection or evals, not only a slide
- Build the training and data path you need (PyTorch, dataset slices, evals) instead of waiting for a platform team
- Cover research and engineering. There is no intern track that is only notebooks

### YOU MAY BE A GOOD FIT IF YOU HAVE (MUST-HAVE QUALIFICATIONS)

- Progress toward a Bachelor’s, Master’s, or PhD (preferred) in CS, EE, or a related field
- You have trained real models (course, lab, open source, or a paper). PyTorch or equivalent
- You want in-the-wild physical or video data, not only academic splits
- You can operate independently on a 12-week bet
- You will be in San Francisco, in person

### STRONG CANDIDATES MAY ALSO HAVE EXPERIENCE WITH (NICE-TO-HAVE QUALIFICATIONS)

- Video, world models, robotics, or multimodal training
- Egocentric or in-the-wild video
- Large-scale training, dataset curation, or distributed GPU work
- Publication at a top conference (CVPR, NeurIPS, ICML, ICLR, RSS, CoRL, or equivalent)
- A paper, open-source model, or project that changed what you did next

### BENEFITS

- Competitive pay
- Medical, dental, and vision packages with generous premium coverage
- $500 per month credit for waiving medical benefits
- Housing subsidy of $2k per month for those living within walking distance of the office
- Relocation support for those moving to San Francisco (Financial District) or Shenzhen (Nanshan)
- Various wellness benefits covering fitness, mental health, and more
- Daily lunch and dinner in our office
- Unlimited compute budget subject to ROI justification
- Unlimited Codex and Claude credits
- Travel

### HOW WE'RE DIFFERENT

Build believes in the Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html. By taking a general approach of learning from humans, our addressable market is all physical labor. We are a fully in-person team in San Francisco (Financial District) and Shenzhen (Nanshan), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.

Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: research@build.ai

From Build AI's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.

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More new-grad and intern roles: https://newgrad.ai/llms.txt