# Product Lead at Build AI > Onsite · United States · Staff · Other · open. Source: https://alderhq.app/jobs/ashby%3Abuild-ai%3Acf5746e9-e130-494a-b7bc-8e528d8eda21 (read 2026-11-04). ## 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 (67 days ago) - First seen by alder: 2026-11-03 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/build-ai/cf5746e9-e130-494a-b7bc-8e528d8eda21 ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 666): p25 $180k, median $214k, p75 $250k ## 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 are hiring a Product Lead to own the product that applies deep learning to physical labor businesses and then scales it country by country. This is not a VP of Product seat. You decide what we ship, who it is for, and how it gets into the hands of operators doing the real job — collection hardware, the customer product, and the field workflows in between. ### Key Responsibilities Define and ship the product that brings models trained on in-the-wild labor data into real workplaces Sit at the intersection of research, software, Shenzhen hardware, and field ops so collection, models, and the customer product are one system, not three roadmaps Own 0→1 site and country expansion: standup checklists, operator UX, device + software kits, and the path from first factory to repeatable rollout Talk to operators and customers, then turn that into a roadmap you actually ship — not a deck Set quality, latency, and operational bars so the product works on a job site, not only in a demo Work with Data Compliance and marketplace incentives so consent, pay, and collection live in the product, not as afterthoughts ### You may be a good fit if you have (Must-have qualifications) You have owned a product 0→1, ideally in a messy real-world or operations-heavy domain Comfortable with ML / deep learning as a product surface, not only as a research paper You can work with engineers and field/ops teams without a large product org under you Bias toward shipping and scaling, including internationally You want to solve physical labor, not build another dashboard company ### Strong candidates may also have experience with (Nice-to-have qualifications) Experience standing up operations or deployments for a physical product at an early-stage company Familiarity with data collection or evaluation workflows for ML Experience taking a product into new countries without a large product org You have hired or managed engineers as a player-coach ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Abuild-ai%3Acf5746e9-e130-494a-b7bc-8e528d8eda21 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.