# Software Engineer, Generalist at Build AI > Onsite · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Abuild-ai%3A30e6ec54-c689-4b47-b8d2-6a0df9128e4e (read 2026-11-05). ## 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/30e6ec54-c689-4b47-b8d2-6a0df9128e4e - Skills listed (inferred): TypeScript, React, Python, Go, C++, Docker ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 1495): p25 $161k, median $200k, p75 $247k ## 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 generalist software engineers who can work across the company. This seat finds the gap, ships in days not weeks, and moves on. One week you might build a data pipeline; the next a hardware integration script, an internal tool, or a customer-facing API. The only constant is a problem that is time-sensitive and business-critical. ### Key Responsibilities Detect the highest-leverage gap that week — ingest, APIs, orchestration, field tools, hardware glue — and ship production-quality code for it Build backend services and APIs for the data platform and the customer-facing product Write processing and validation pipelines for incoming in-the-wild datasets Script integrations between collection devices, cloud infrastructure, and software services Design interfaces and developer workflows that the rest of the company actually uses Work cross-functionally with research, Shenzhen hardware, and ops without waiting for a spec ### You may be a good fit if you have (Must-have qualifications) Strong full-stack skills; fluency in Python and at least one of TypeScript, React, Go, or C++ 1–5+ years shipping software end to end, ideally in a 0→1 or startup environment You ramp quickly in unfamiliar codebases and problem domains Bias toward action and comfort with incomplete information Experience shipping where the role was not clearly defined ### Strong candidates may also have experience with (Nice-to-have qualifications) Experience across backend, frontend, data, infra, and scripting Hardware-adjacent, AI, or large-scale data systems Prior startup experience during hypergrowth Infrastructure or DevOps (Linux, containers, cloud) ### 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%3A30e6ec54-c689-4b47-b8d2-6a0df9128e4e and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.