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At a glanceSummarised by Seekless from the posting.
Must have4
Quantitative: you can take someone else’s objective function and optimize against it
Comfortable designing and reading experiments (assignment, holdouts, power, leakage)
Comfortable with messy field / collection data
You like iteration: small changes, measured against the objective, shipped again
Nice to have3
Experience with marketplaces, incentives, pricing, or growth experimentation (gig, two-sided, or similar)
Experience with payouts, bonuses, or pay systems that moved real money
You have shipped experiment results into a product, not only a notebook
Skills
Codex
Claude
Benefits
Health insurance
Dental insurance
Vision insurance
Housing stipend
Relocation assistance
Fitness benefits
Mental health benefits
Meals
Unlimited compute
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
This role exists to hillclimb the dataset objective: the ideal dataset, the objective function, the taxonomy, and the value of incremental data. You take that objective and maximize it: incentives, marketplace design, and collection behavior so the next hour we collect actually moves the score.
Key Responsibilities
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Treat the Dataset & Quality objective / value function as the thing you optimize — not raw hours, not vanity volume
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Design marketplace mechanics (task mix, targeting, bonuses, payouts, eligibility) so collectors produce high-value data
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Run experiments: treatment assignment, holdouts, pay and bonus tests; keep the ones that hillclimb the objective and kill the rest
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Measure whether incremental collection is improving the dataset under the value function, and report that in language ops can act on
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Work with software to ship incentive and payout changes into the collection product (not a spreadsheet that never lands)
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Watch for Goodhart: collectors optimizing the proxy instead of the objective, and close those holes
You may be a good fit if you have (Must-have qualifications)
•
Quantitative: you can take someone else’s objective function and optimize against it
•
Comfortable designing and reading experiments (assignment, holdouts, power, leakage)
•
Comfortable with messy field / collection data
•
You like iteration: small changes, measured against the objective, shipped again
Strong candidates may also have experience with (Nice-to-have qualifications)
•
Experience with marketplaces, incentives, pricing, or growth experimentation (gig, two-sided, or similar)
•
Experience with payouts, bonuses, or pay systems that moved real money
•
You have shipped experiment results into a product, not only a notebook
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
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Relocation support for those moving to San Francisco (Financial District) or Shenzhen (Nanshan)
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Various wellness benefits covering fitness, mental health, and more
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Daily lunch and dinner in our office
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Unlimited compute budget subject to ROI justification
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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.
Legal
Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: [email protected]