QA Reviewer, Data Annotation

Mind Robotics • Palo Alto, CA

1d

About The Position

Mind Robotics is building Physical AI for real-world industrial deployment, starting on the factory floor. The company seeks people passionate about robotics, ownership, and solving difficult problems by working hands-on with the physical world.

The QA Reviewer & Calibration Lead will help ensure the data annotation pipeline operates at high throughput without sacrificing quality. This role is responsible for quality control in the continuous data flywheel, leading alignment sessions, identifying systematic labeling inconsistencies, and working with internal reviewers and partner teams to resolve ambiguous edge cases.

Requirements

• 2–4 years of experience in quality assurance, data annotation QA, or data review in high-velocity technology or AI environments.
• Meticulous attention to detail, including the ability to identify subtle edge cases and resolve ambiguous data scenarios using practical, logical judgment.
• Strong written and verbal communication skills, including the ability to explain complex labeling rules and facilitate alignment discussions.
• Comfort navigating dashboards, spreadsheets, and basic reports to track accuracy metrics and identify trends.
• Proactive, collaborative, team-oriented approach focused on coaching reviewers and continually improving quality.

Nice To Haves

• Experience with multi-camera or ego-exo video annotation, point cloud data, or robotics training data pipelines.
• Experience working with third-party vendor teams (BPO or managed services) in a fast-paced hardware or AI deployment environment.

Responsibilities

• Lead weekly calibration and alignment meetings with internal reviewers and vendor partners to promote consistent data grading and labeling.
• Identify common reviewer errors, conduct root-cause analysis of labeling discrepancies, and build feedback loops to prevent systemic mistakes.
• Maintain clear, current labeling instructions, edge-case taxonomies, and standard operating procedures as project requirements evolve.
• Monitor quality scores, accuracy rates, and inter-annotator agreement metrics through dashboards to ensure production targets are met.
• Provide direct feedback and targeted training to reviewers handling complex guidelines or multi-sensor data edge cases.
• Coordinate with technical leads, operations, and annotation teams to resolve taxonomy questions and keep the data flywheel moving smoothly.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed