BU
Build AIResearch Intern
SalaryNo pay range posted
LocationSan Francisco
Also inShenzhen (Nanshan)
Work modeon-site
Typeinternship
SeniorityEntry
DepartmentResearch
Company size51–200 people
PostedAug 30, 2026 · 1mo ago
Verified live20h ago
At a glanceSummarised by Seekless from the posting.
Progress toward a Bachelor’s degree in CS, EE, or a related field
Progress toward a Master’s degree 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
Progress toward a PhD in CS, EE, or a related field
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
You will be in San Francisco, in person
Skills
PyTorch
Codex
Claude
Benefits
Health insurance
Dental insurance
Vision insurance
Housing stipend
Relocation assistance
Gym membership
Meal plan
Unlimited compute
Unlimited codex credits
Unlimited claude credits
Travel
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. 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.
Legal
Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: [email protected]