Build Ai

Quantitative, Head of Dataset & Quality

San Francisco, California, United States · On-site · FullTime

USD 250,000–500,000/year

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About the role

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EmployerBuild Ai
LocationSan Francisco, California, United States
Work styleOn-site
Employment typeFullTime
Employer posted1 month ago
Salary (as stated by the employer)USD 250,000 – 500,000 per year

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 is not “make the data prettier.” You own whether the dataset is actually solving physical labor. The objective is to maximize the bandwidth of net-new learnable information into the dataset: what we should collect, how we score what we have, and whether the next hour of collection adds information or just volume.

You define the ideal dataset, the objective function on the current dataset, the taxonomy, and the value function for incremental data. Collection strategy comes from those, including the bet that scaling simple, high-bandwidth capture (real workers, real jobs, a camera) beats slower high-fidelity setups.

Show full description

A quant background is the default profile.

Key Responsibilities

Define the ideal dataset for solving physical labor (coverage, diversity, what “done” looks like) and the objective function on what we have now

Design the taxonomy collectors actually use, plus golden sets, acceptance criteria, and audit so quality is measurable

Create the value function for incremental data: given what we have, what is the next example worth?

Turn that into collection strategy (where, which work, how much, when to stop) and into standards ops and vendors execute against

Resist false local optima: extra sensors, extra fidelity, extra process that cuts throughput and net information

Work with marketplace incentives, software, and research so the objective is in the loop — scorecards on coverage, quality, and information, not only volume

Partner with Evals Lead so dataset decisions and model-capability numbers inform each other

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

Quant background (quant research, statistics, decision science, or similar). You think in objective functions and information, not only in label-quality queues

You can argue about scaling vs fidelity with numbers

Experience with dataset design, collection strategy, or large-scale data programs

Fine with in-the-wild collection and a company still scaling countries

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

Experience at a lab, quant fund, or large-scale data program

You have designed a taxonomy, golden set, or coverage model used in production

Familiarity with in-the-wild collection, video, or pose data

Experience setting quality standards and audit processes that collectors or vendors actually hit

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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