About this role
About the Role
We are looking for a detail-oriented QC Reviewer to ensure the quality and consistency of robot manipulation video annotations produced by our annotation team. You'll review labeled video segments against a detailed style guide and reference examples, catch errors or inconsistencies, and provide clear, constructive feedback to annotators. This role is critical to maintaining the accuracy and reliability of training data used for robotics AI models.
What You'll Do
- Review completed annotations of robot manipulation videos across three levels of labeling detail (atomic motion, skill/subtask, task/goal).
- Cross-check annotator descriptions against all three camera views (overhead and both wrist-mounted cameras) to verify accuracy of described actions, object identification, and contact/grasp details.
- Verify full coverage of each video — confirm there are no gaps in labeling, including idle or pause moments.
- Check that descriptions accurately reflect what actually happened in the video, including failures, drops, or slipped grips — flagging any instances where outcomes were inaccurately described or "smoothed over."
- Evaluate annotations for adherence to the style guide: correct vocabulary usage, spatial/directional accuracy, consistent terminology for objects and actions, and appropriate language variation (avoiding repetitive phrasing).
- Identify recurring errors or patterns of confusion among annotators and report them to the team lead for calibration purposes.
- Provide specific, actionable feedback on rejected or revised annotations, referencing the style guide and reference examples.
- Participate in periodic calibration sessions with the team and client to align QC standards with evolving guidelines or edge-case resolutions.
- Maintain a QC log or tracking sheet documenting review outcomes, common error types, and annotator performance trends (as required by the project).
What We're Looking For