Project Cursa - Robot Manipulation Video Annotation QC (V2)

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

Required:

- Excellent written English and strong reading comprehension — you'll be 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.

Nice to Have:

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

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

- Familiarity with robotics terminology (grippers, end-effectors, manipulation).

- Experience with annotation tools such as Label Studio, including review/approval workflows.

#L1-CC1

Why Join Welo Data?

✨ Limitless Flexibility

Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.

🌱 Limitless Growth