Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
We are looking for a QA Reviewer & Calibration Lead to ensure our data annotation pipeline operates at high throughput without sacrificing quality. In this role, you will be the backbone of quality control for our continuous data flywheel. You will lead regular alignment sessions, identify systematic labeling inconsistencies, and work hands-on with internal reviewers and partner teams to resolve ambiguous edge cases.
Responsibilities:
Run calibration sessions: Lead weekly alignment meetings with internal reviewers and vendor partners to ensure everyone grades and labels data with high consistency.
Track and fix quality issues: Spot common reviewer errors, conduct root-cause analysis on labeling discrepancies, and build feedback loops to eliminate systemic mistakes.
Update guidelines and documentation: Keep labeling instructions, edge-case taxonomies, and SOPs clear, crisp, and up-to-date as project requirements evolve.
Monitor quality metrics: Track quality scores, accuracy rates, and inter-annotator agreement metrics across dashboards to ensure production targets are met.
Provide feedback and guidance: Deliver direct feedback and targeted training to reviewers navigating complex guidelines or multi-sensor data edge cases.
Coordinate cross-functionally: Work closely with technical leads, operations, and annotation teams to resolve taxonomy questions quickly and keep the data flywheel moving smoothly.
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: A sharp eye for subtle edge cases and a practical, logical approach to resolving ambiguous data scenarios.
Clear communication: 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 mindset: A proactive, team-oriented attitude focused on coaching reviewers and continually driving quality across the organization.
Experience with multi-camera/ego-exo video annotation, point cloud data, or robotics training data pipelines (is a plus).
Background working with third-party vendor teams (BPO/managed services) in a fast-paced hardware or AI deployment environment (is a plus).