The board
Figure · Data Operations- Where
- San Jose, CA
- Posted
- Jul 11
Figure is an AI Robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It’s time to build.
Figure AI is building the data foundation that powers our humanoid robots. The Data Quality team owns the standards, guidelines, and audit infrastructure that ensure our training data meets the bar our AI systems require.
As a Project Coordinator, you'll own a full project lifecycle within Data Quality — from defining review guidelines to running audit cycles to managing the analysts and vendors who execute the work.
What you'd do
- Own review guidelines and acceptance criteria for your project; handle escalated edge cases that fall outside existing guidance.
- Contribute to golden set construction for your project type.
- Run project-level audit cycles and feed findings directly back into guideline updates.
- Coordinate analysts across projects within your scope.
- Write and maintain project-level SOPs; update them through post-project retros.
- Manage direct reports where applicable, including structured performance reviews.
- Design project-specific training materials and calibration exercises.
- Serve as the primary contact for project-level vendor coordination, including communicating SLA expectations.
- Translate quality requirements into actionable review guidelines; act as first point of contact for project-level clarifications.
What they want
- 4+ years of experience in data quality, data labeling operations, or content/data review roles, with demonstrated ownership of a project or workstream end-to-end.
- Experience writing review guidelines, acceptance criteria, or SOPs that other people executed against.
- Experience running audit or QA cycles and translating findings into process or guideline changes.
- Comfort working directly with engineering or ML stakeholders to translate technical requirements into operational guidance.
- Experience managing vendor relationships, including SLA communication.
- People management experience, or readiness to take on direct reports.
- Experience with golden set / ground truth construction.
- Background in robotics, autonomous systems, LLM or physical AI data.
- Experience designing training or calibration programs for review teams.