Project Cursa - Robot Manipulation Video Annotation QC

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'llreview labeled video segments against a detailedstyleguide and reference examples, catch errors or inconsistencies, and provide clear, constructive feedback to annotators. This role is critical tomaintainingthe accuracy and reliability of training data used for robotics AI models.

• 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 happenedin 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 languagevariation (avoiding repetitive phrasing).

• Identify recurring errors or patterns of confusion among annotators and report them to theteamleadfor 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 andclientto align QC standards with evolving guidelines oredge-caseresolutions.

• Maintain a QC log or tracking sheet documenting review outcomes, common error types, and annotator performance trends (as required by the project).

• Excellent written English and strong reading comprehension
  -you'llbe evaluating precise, nuanced language choices, not just checking for typos.

• Exceptional attention to detail - able to catch subtle mismatches between video content and written description (e.g., a mislabeled grasp type, an incorrect object part, a missed failure event).

• Strong grasp of spatial/mechanical description (left/right, above/below, object parts like handles, lids, edges) to verify annotator accuracy.

• Ability to apply a detailed, structured style guide consistently and make sound judgment calls in ambiguous or edge-case scenarios.

• Comfort giving clear, specific, and constructive feedback to annotators (written feedback, not just pass/fail marks).

• Reliable, self-directed work habits, with the ability to work independently while meeting review turnaround expectations.

• Prior experience in a QC, editing, proofreading, or annotation review role.

• Experience with video annotation, data labeling, or transcription QA specifically.

• Experience with annotation tools such as Label Studio, including