QA Reviewer, Data Annotation
QA Reviewer & Calibration Lead who quality-controls Mind Robotics' data annotation pipeline for Physical AI/robotics training data: runs weekly calibration sessions with reviewers and vendors, tracks accuracy and inter-annotator agreement metrics, updates labeling guidelines, and coaches reviewers on edge cases. On-site in Palo Alto.
- Compensation
- Not disclosed
- Location
- Palo Alto
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Qa
- Posted
Skills
Must have
- 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; sharp eye for subtle edge cases and ambiguous data scenarios
- Strong written and verbal communication; able to explain complex labeling rules and facilitate alignment discussions
- Data fluency: comfortable navigating dashboards, spreadsheets, and basic reports to track accuracy metrics and spot trends
- Collaborative, proactive mindset focused on coaching reviewers and driving quality
Nice to have
- Experience with multi-camera/ego-exo video annotation, point cloud data, or robotics training data pipelines
- Background working with third-party vendor teams (BPO/managed services) in a fast-paced hardware or AI deployment environment
Full description
QA Reviewer, Data Annotation
About Mind:
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.
About the team and the role:
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.
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: 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.