Project Coordinator, Data Quality

Figure; San Jose (CA); On-site; USD 100,289,280 - 114,616,320; Full time

Job summary

Figure is seeking a Project Coordinator in Data Quality to own complete project lifecycles—from defining review guidelines to managing audit cycles, analysts, and vendors.

You will translate requirements into actionable guidance, oversee edge cases, and help shape calibration programs. The role blends data quality leadership with vendor and cross‑team collaboration to maintain high standards.

Qualifications

• 4+ years in data quality, labeling ops, or content review with end-to-end ownership.
• Experience writing review guidelines, acceptance criteria, or SOPs.
• Experience running audit/QA cycles and translating findings into changes.
• Able to work with ML/engineering to translate requirements into guidance.
• Vendor management experience including SLA communication.
• People management experience or readiness to take on direct reports.

Responsibilities

• Own review guidelines and acceptance criteria for your project; handle escalated edge cases.
• Contribute to golden set construction for your project type.
• Run project-level audit cycles and feed findings back into guideline updates.
• Coordinate analysts across projects within your scope.
• Write and maintain project-level SOPs; update them through retros.
• Manage direct reports where applicable, including performance reviews.
• Design project-specific training materials and calibration exercises.
• Serve as the primary contact for project-level vendor coordination, including SLA communications.
• Translate quality requirements into actionable review guidelines; act as a first contact for clarifications.

Skills

Data quality experience

Data labeling operations

Content review

Project ownership

Stakeholder communication

Job description

Overview

Figure is an AI Robotics company focused on building autonomous general‑purpose humanoid robots. The data foundation team ensures that training data meets the high standards required by our AI systems. As a Project Coordinator in Data Quality, you will own complete project lifecycles, from defining review guidelines to managing audit cycles, analysts, and vendors.

Responsibilities

• 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.

Requirements

• 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.

Bonus Qualifications

• 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.

Compensation

The US base salary range for this full‑time position is between $35 – $40 per hour. Pay may vary based on individual factors, including job‑related knowledge, skills, and experience. Total compensation may include additional components/benefits depending on the specific role, and this information will be shared if an employment offer is extended.