Project Cursa - Robot Manipulation Video Annotation QC (V2)

Welocalize, Inc.; Malaysia; Remote; MYR 28,000 - 55,000; Full time

Job summary

Welocalize, Inc. seeks a detail-oriented QC Reviewer to ensure the quality and consistency of robot manipulation video annotations.

You will review labeled segments against a thorough style guide, catch errors, and provide constructive feedback to annotators to maintain training data accuracy for robotics AI. Work is project-based and fully remote, with opportunities to participate in calibration sessions and expand expertise in robotics terminology and data labeling practices.

Qualifications

• Excellent written English and strong reading comprehension.
• Exceptional attention to detail to catch subtle mismatches between video content and written description.
• Strong grasp of spatial/mechanical description to verify annotator accuracy.
• Ability to apply a detailed, structured style guide consistently.

Responsibilities

• Review robot manipulation video annotations at three labeling levels (atomic motion, skill/subtask, task/goal).
• Cross-check annotator descriptions against all three camera views to verify accuracy of actions, objects, and grips.
• Verify full video coverage with no gaps in labeling, including idle moments.
• Flag discrepancies where descriptions misstate outcomes or are smoothed over.
• Ensure vocabulary and terminology align with the style guide and vary language to avoid repetition.
• Identify recurring errors and report patterns to the team lead for calibration.
• Provide actionable feedback referencing the style guide for rejected or revised annotations.
• Participate in calibration sessions with the team and client to align QC standards.
• Maintain a QC log documenting review outcomes and annotator trends.

Skills

Attention to detail

Written English

Spatial reasoning

Style-guide application

Constructive feedback

Independent work

Video annotation familiarity

Label Studio experience

Tools

Label Studio

Job description

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.

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 whatactually 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, andappropriate languagevariation (avoiding repetitive phrasing).
• Identifyrecurring 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).

What We're Looking For

Required:

• 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.

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.

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

Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.

Be part of a global contributor community with responsive guidance and support.

Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.