# Member of Technical Staff at Build AI

> Onsite · United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Abuild-ai%3Aa060de84-162a-4a97-b080-d8792ba242b4 (read 2026-11-13).

## Facts (observed)

- Company: [Build AI](https://alderhq.app/companies/build-ai)
- Location text: San Francisco
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco")
- Posted: 2026-08-30 (76 days ago)
- First seen by alder: 2026-11-03 · last verified: 2026-11-09
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/build-ai/a060de84-162a-4a97-b080-d8792ba242b4
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 1161): p25 $205k, median $240k, p75 $284k

## Description (the employer's text)

### 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 Members of Technical Staff — the general research/engineering seat. If you have a seriously compelling research bet you want to run on physical-labor, video, or world-model data, you might as well do it at Build. We have the dataset, we are scaling it by orders of magnitude, and a small team that will actually let you train on it.

You are not hired to polish a narrow job description. You are hired to pursue the bet, and to help us see what the data is teaching.

### Key Responsibilities

Run research bets on Build data: train, evaluate, ablate, and ship what works

Design and train models on in-the-wild video and physical-labor data; figure out what actually gets learned as we scale, and what is still missing

Build the training and data paths you need (PyTorch, distributed training, dataset curation) instead of waiting for a platform team

Work with Head of Dataset & Quality and Evals Lead so your results change collection and evals, not only a paper

Cover research and engineering as needed in a team under 30

Design evaluation frameworks for your bets that are honest about generalization, not only a training curve

### You may be a good fit if you have (Must-have qualifications)

You’ve trained real models. You have a research bet you could defend

Proficiency in Python and a deep learning framework (PyTorch or equivalent)

You want to work on in-the-wild physical data, not only academic splits

You can move between research questions and engineering to get an experiment done

Ability to operate independently on an ambiguous, high-impact direction

### Strong candidates may also have experience with (Nice-to-have qualifications)

Video, world models, robotics, or multimodal training

Experience with large-scale pretraining, dataset curation, or distributed GPU training

Familiarity with egocentric / first-person or in-the-wild video

Published work or shipped models that changed what a lab 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: research@build.ai

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Abuild-ai%3Aa060de84-162a-4a97-b080-d8792ba242b4 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.