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Active2mo ago

Software Engineer, Scaling

Build AI·📍 San Francisco, California, United States

Employment

FULL TIME

Work mode

On-site

Experience

Not stated

Salary

$180K – $300K/yr

Pay vs market · Below median

This job's pay ($240,000 a year at the midpoint) is higher than about 40% of 584 salary-listed Software Engineer jobs in San Francisco in the last 12 months; their median is $263,000.

Salary data →

Role overview

Job description

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 a software engineer to build the products the on-the-ground scaling teams live in. Your users are the people standing up collection in factories and job sites. If a site cannot collect more tomorrow because the software is in the way — tasking, device health, local tooling, site standup — that is on you. Key Responsibilities Build internal products for collection ops: tasking, site standup, device inventory, shift tracking, and local tooling that works with bad connectivity Sit with ops and watch where they lose hours; ship software that removes it, then measure hours of useful collection per person Make standing up a new site or country a repeatable product, not a custom process each time Produce site-health views: what’s recording, what’s failing, what’s stuck in upload — so field leads and SF engineering share one picture Work with data infrastructure and marketplace incentives so field tools connect to ingest and pay Travel or embed with field teams when that is what it takes to get the product right You may be a good fit if you have (Must-have qualifications) Strong generalist software engineer who likes shipping product for operators, not only backend services Comfortable with messy real-world ops and 0→1 tools Fine traveling or working closely with field teams You measure success in whether scaling teams scale harder, not in polished abstractions Strong candidates may also have experience with (Nice-to-have qualifications) Time spent embedded with field, ops, logistics, or deployment teams Experience standing up tools in new sites or countries Marketplace, incentive, or ops-product background Linux/Python and comfort debugging hardware-adjacent field issues 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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About this listing

  • Source: Build AI's official careers site · still listed there on 9 Nov 2026
  • Posted 30 Aug 2026

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Role market pulse

How Software Engineer demand looks in United States

83/100 · hot

Live jobs

3,423

Posted 30d

2,600

30d movement

+484.3%

Remote share

26.2%

Salary listed

83.6%

Salary trend 1Y

Not enough history

What they are looking for

Skills & requirements

Department · Software

Hiring company

Build AI

Technology

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 more than anyone in the world.

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