Quantitative, Head of Dataset & Quality
About Build AI
Build AI is the data hyperscaler for Physical AI. We co-design hardware, collection, infrastructure, and research to scale the in-the-wild physical labor dataset by orders of magnitude. We learn from humans doing the real job, in real environments. Inflecting revenue, backed by top-tier investors and staffed by leading engineers, Build is becoming the bottleneck to solving physical labor.
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.
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
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
Travel
How we're different
Build believes in the Bitter Lesson. We are betting early on learning from real human work at massive scale, and that the economies of scale of collection beat extra sensors and extra fidelity. Our addressable market is all physical labor, unlike many of our competitors.
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