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Mind Robotics is building Physical AI systems for real-world industrial deployment, starting with factory-floor automation. The company believes that solving the hardest AI problems requires researchers and engineers to be hands-on with the physical world daily.
You will serve as QA Reviewer & Calibration Lead, ensuring the data annotation pipeline operates at high throughput without sacrificing quality. You are the backbone of quality control for the continuous data flywheel that trains and improves the robotics systems.
Key responsibilities include:
- Running calibration sessions: Lead weekly alignment meetings with internal reviewers and vendor partners to ensure consistent grading and labeling standards across all annotators.
- Tracking and fixing quality issues: Identify common reviewer errors, conduct root-cause analysis on labeling discrepancies, and build feedback loops to eliminate systemic mistakes before they propagate.
- Updating guidelines and documentation: Keep labeling instructions, edge-case taxonomies, and standard operating procedures clear and current as project requirements evolve.
- Monitoring quality metrics: Track quality scores, accuracy rates, and inter-annotator agreement metrics across dashboards to ensure production targets are met.
- Providing feedback and guidance: Deliver direct feedback and targeted training to reviewers working with complex guidelines or multi-sensor data edge cases.
- Coordinating cross-functionally: Work closely with technical leads, operations, and annotation teams to resolve taxonomy questions quickly and maintain smooth data pipeline operations.
You will work with both internal teams and third-party vendor partners, managing alignment across distributed annotation efforts. The role requires hands-on involvement in quality assurance while building scalable processes.
REQUIREMENTS:
- 2–4 years of experience in quality assurance, data annotation QA, or data review in high-velocity tech or AI environments
- Meticulous attention to detail with a sharp eye for subtle edge cases and a practical, logical approach to resolving ambiguous data scenarios
- Strong written and verbal communication skills with the ability to explain complex labeling rules clearly and facilitate alignment discussions
- Data fluency: comfortable navigating dashboards, spreadsheets, and basic reports to track accuracy metrics and spot trends
- Collaborative, proactive, team-oriented mindset focused on coaching reviewers and driving quality across the organization
- Plus: Experience with multi-camera/ego-exo video annotation, point cloud data, or robotics training data pipelines
- Plus: Background working with third-party vendor teams (BPO/managed services) in a fast-paced hardware or AI deployment environment
