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Build AI

Public-benefit data hyperscaler for physical AI

Software Engineer - Data Visualization

Frontend EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$180k - $300k /yr

About the Job

Requirements

  • Strong software engineering background with a track record in data visualization, internal tools, or product user interfaces involving large datasets.
  • Comfort working with large media such as video, pose, or similar data.
  • Ability to work in an environment where requirements and processes are still being defined.

Responsibilities

  • Design and ship a visualization suite for data collection, processing, analysis, and user interfaces on the web and/or command line for the dataset and data engine.
  • Make it fast to inspect examples, batches, coverage, quality, and failures so research and operations teams use the tools.
  • Build views for collection health, taxonomy coverage, and evaluation results, connected to live pipelines rather than one-off dashboards.
  • Work with the Head of Dataset & Quality, Evals Lead, research, and operations to determine what information needs to be visible to support decisions.
  • Treat visualization as a product by optimizing latency, defaults, shareability, and presentation of video and pose.

Desired Qualifications

  • Experience with WebGL, custom renderers, or high-performance browser visualization.
  • Experience shipping internal tools used by other teams.
  • Experience building analysis user interfaces, including timelines, slice explorers, and statistical summaries.
  • Python and TypeScript/React experience.

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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