AI Video QC Annotator

Welo Data; United States; Remote; USD 4,100 - 6,900; Full time

Job summary

Welo Data is seeking a detail-oriented QC Reviewer to ensure quality and consistency of robot manipulation video annotations. You will audit labeled segments against a comprehensive style guide, verify accuracy across multiple camera views, and provide constructive feedback to annotators to maintain high data reliability for robotics AI models.

You will review annotations for completeness and consistency, identify recurring error patterns, and contribute to calibration sessions with the team and

Qualifications

• Excellent written English and strong reading comprehension to evaluate precise language choices.
• Exceptional attention to detail to catch subtle mismatches between video content and descriptions.
• Strong grasp of spatial/mechanical description to verify annotator accuracy.
• Ability to apply a detailed, structured style guide and make sound judgments in edge cases.

Responsibilities

• Review robot manipulation video annotations across multiple labeling levels.
• Cross-check annotations against three camera views for accuracy of actions, objects and contact.
• Verify full video coverage with no gaps in labeling, including idle moments.
• Flag discrepancies where outcomes are described inaccurately or smoothed over.
• Provide actionable, specific feedback referencing the style guide and reference examples.
• Identify recurring errors and report them for calibration and improvements.
• Maintain a review log documenting outcomes and trends.

Skills

Attention to detail

Strong written English

Detailed style guide

Clear feedback

Independent worker

Job description

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'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.

Project Details:

· Job Title: Project Cursa - Robot Manipulation Video Annotation QC

· Location: Remote in India/Philippines/Mexico/Egypt

· Employment type: Freelance

· Pay Rate: US$4.03/hour

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.