San Francisco, CA, USA+1 moreMore locations: Shenzhen, Guangdong Province, China
In PersonFully in-person in San Francisco (Financial District) and Shenzhen (Nanshan); the role qualifications state the intern will be in San Francisco.
AI & Machine LearningAI Research
About the job
Requirements
Make progress toward a bachelor's, master's, or PhD in Computer Science, Electrical Engineering, or a related field.
Have trained real models through coursework, a lab, open source, or a paper.
Be willing to work with in-the-wild physical or video data, not only academic splits.
Be able to operate independently on a 12-week research project.
Be in San Francisco and work in person.
Responsibilities
Run a research project on Build data, including training, evaluation, ablation, and documenting what the data taught.
Work with researchers, dataset, and evaluation teams so the internship informs data collection or evaluations.
Build the required training and data pipeline, including PyTorch, dataset slices, and evaluations.
Contribute to both research and engineering, rather than working only in notebooks.
Desired Qualifications
Experience with video, world models, robotics, or multimodal training.
Experience with egocentric or in-the-wild video.
Experience with large-scale training, dataset curation, or distributed GPU work.
Publication at a leading conference such as CVPR, NeurIPS, ICML, ICLR, RSS, or CoRL, or an equivalent conference.
A paper, open-source model, or project that influenced subsequent work.
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.
Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: [email protected]