We are sharing a specialised part-time consulting opportunity for experienced AI training and data annotation professionals with strong expertise in video annotation, data labelling, timestamping, and visual quality review to contribute to an advanced robotics and AI training project.
Selected professionals will review video footage of robotic systems, identify and annotate key actions, apply detailed grading guidelines, and produce precise timestamped datasets that support improvements in AI model accuracy. The work requires exceptional attention to detail, consistent application of annotation standards, and confidence working with frame-level or timecode-based video review.
Key Responsibilities
Video Annotation & Event Identification
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Review video footage of robotic arms performing assigned tasks
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Identify key actions, transitions, outcomes, and relevant events with precision
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Apply structured grading and annotation guidelines consistently across video sessions
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Tag important behaviours and observations that support downstream AI training
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Maintain high accuracy across repeated and detail-intensive annotation tasks
Timestamping & Frame-Level Review
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Mark precise timestamps for the start and end of key actions and transitions
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Perform frame-accurate segmentation of video footage where required
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Verify alignment between timestamps, labelled events, and underlying video content
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Identify and correct annotation drift, timing inconsistencies, or incorrect event boundaries
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Maintain consistent timecode and segmentation standards across assigned datasets
Annotation Tools & Data Submission
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Use video annotation platforms and timeline-based interfaces to record structured findings
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Submit annotated datasets according to project standards, milestones, and technical requirements
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Review completed annotations for accuracy and completeness before submission
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Follow defined workflows for data labelling, quality control, and annotation management
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Adapt to evolving annotation tools, interfaces, and project-specific requirements
Quality Assurance & Project Collaboration
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Participate in ongoing quality reviews and incorporate feedback into future work
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Collaborate with project trainers and other contributors to resolve ambiguous annotation cases
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Help refine annotation guidelines and improve consistency across the broader project team
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Communicate progress, issues, and quality concerns clearly and promptly
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Apply analytical judgement when resolving difficult or unclear annotation scenarios
Ideal Profile
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Proven experience with video annotation, data labelling, or similar data-centric annotation work
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Exceptional attention to detail and accuracy when reviewing visual information
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Familiarity with video annotation tools and timeline-based interfaces
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Experience with frame-level or timecode-based annotation is strongly preferred
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Ability to identify event boundaries and review footage frame by frame when required
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Strong written and verbal communication skills
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Good time-management and self-organisation skills for independent remote work
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Analytical and problem-solving mindset with a proactive approach to annotation challenges
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Experience in AI training, machine-learning data preparation, robotics, or related projects is strongly preferred
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Ability to follow detailed guidelines and maintain consistent quality across repetitive tasks
Engagement Details
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Part-time independent contractor engagement
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Fully remote
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Compensation: $50–$90/hour
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Work will involve robotics video review, event annotation, timestamping, data labelling, and quality assurance
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Strong attention to detail and consistency are important for this engagement
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Experience with frame-level annotation and video review tools is highly relevant
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Project scope, workload, timing, and duration may evolve depending on project requirements
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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