QA Reviewer, Data Annotation

Mind Robotics; Palo Alto (CA); On-site; USD 90,000 - 140,000; Full time

Job summary

Mind Robotics seeks a QA Reviewer & Calibration Lead to ensure data annotation throughput while maintaining high quality. You will lead calibration sessions with internal reviewers and vendor partners, identify and fix labeling issues, and keep guidelines current as project needs evolve.

You will monitor quality metrics, provide targeted feedback and coaching, and collaborate across technical leads, operations, and annotation teams to keep the data flywheel moving smoothly.

Qualifications

• 2–4 years of experience in QA, data annotation QA, or data review in high-velocity AI environments.
• Meticulous attention to detail and ability to resolve edge cases.
• Strong written and verbal communication to explain labeling rules clearly.
• Data fluency with dashboards, spreadsheets, and basic reports.
• Collaborative mindset with coaching reviewers and driving quality across the org.
• Experience with multi-camera/video annotation or robotics datasets is a plus.
• Experience with vendor teams (BPO/managed services) in hardware/AI deployments is a plus.

Responsibilities

• Run calibration sessions: Lead weekly alignment meetings to ensure consistent data grading and labeling.
• Track and fix quality issues: Identify errors, perform root-cause analysis, and build feedback loops.
• Update guidelines and documentation: Keep labeling instructions and SOPs up-to-date.
• Monitor quality metrics: Track accuracy and inter-annotator agreement across dashboards.
• Provide feedback and guidance: Deliver targeted training to reviewers on complex guidelines.
• Coordinate cross-functionally: Work with technical leads, operations, and annotation teams.

Skills

Quality assurance

Data annotation QA

Attention to detail

Clear communication

Tools

Dashboards

Spreadsheets

Reports

Job 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.

• 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).