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Build AI

Public-benefit data hyperscaler for physical AI

Data Scientist - Marketplace Incentives

Data ScienceConfirmed live in the last 24 hours
Full-time$180k - $280k /yr

About the Job

Requirements

  • Ability to optimize against an objective function.
  • Comfort designing and interpreting experiments, including assignment, holdouts, power, and leakage.
  • Comfort working with messy field and collection data.
  • Willingness to iterate through small changes, measure results against the objective, and ship again.

Responsibilities

  • Optimize the Dataset & Quality objective or value function rather than raw hours or volume.
  • Design marketplace mechanics, including task mix, targeting, bonuses, payouts, and eligibility, to encourage production of high-value data.
  • Run treatment-assignment, holdout, pay, and bonus experiments; retain approaches that improve the objective and stop those that do not.
  • Measure whether incremental collection improves the dataset under the value function and report findings in actionable terms for operations.
  • Work with software teams to ship incentive and payout changes into the collection product.
  • Identify and address cases where collectors optimize a proxy rather than the underlying objective.

Desired Qualifications

  • Experience with marketplaces, incentives, pricing, or growth experimentation, including gig, two-sided, or similar settings.
  • Experience with payouts, bonuses, or pay systems involving real money.
  • Experience shipping experiment results into a product rather than only a notebook.

About the Company

Build AI

Public-benefit data hyperscaler for physical AI

Build AI runs as a public benefit corporation that acts as a data hyperscaler for physical artificial intelligence, handling hardware, data collection, and model training in one end-to-end pipeline. It collects an egocentric dataset of physical labor by equipping thousands of workers with head-mounted cameras in real-world settings, then provides this data to researchers and corporate robotics teams to train physical AI models. The company differentiates itself by being vertically integrated—owning the hardware, data collection, and training process—and by focusing on large-scale, real-world data rather than relying on third-party sources. Its goal is to accelerate the development of robots that can perform manual tasks in everyday human environments, unlocking human potential.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
San Francisco, CA
Founded
2025

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