Remote Video Annotation Expert for AI Training

RemoteJobsOne

Charlotte (NC)

Remote

USD 21,000 - 110,000

Part time

38 hours ago
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Job summary

RemoteJobsOne is seeking a Video Annotation Expert for a fully remote contractor role in the United States. You will review and annotate robotics-related video data to train AI systems, with no prior AI experience required beyond your domain knowledge.

You will assess action accuracy, refine annotations, and maintain high-quality documentation following project rubrics and guidelines. This role emphasizes precise evaluation of movement and object interactions across video samples.

Qualifications

  • Experience with video annotation workflows.
  • Ability to apply domain knowledge to AI training data.

Responsibilities

  • Review and analyze short video clips of robots or people performing actions, assessing alignment with provided written directives.
  • Determine the accuracy and completeness of actions based on defined end states and project guidelines.
  • Compare video samples to identify which better fulfills the specified instruction.
  • Refine, correct, or augment AI-generated video annotations, including action boundaries, descriptions, and feedback points.
  • Detect subtle differences in movement, object interaction, and action sequences for precise evaluation.
  • Document observations and decisions using clear written communication.
  • Apply detailed rubrics and maintain consistent annotation quality throughout the assignment.

Skills

Video annotation
Robotics domain knowledge

Job description

RemoteJobsOne is seeking a Video Annotation Expert for a fully remote contractor role in the United States. You will review and annotate robotics-related video data to train AI systems, with no prior AI experience required beyond your domain knowledge.

You will assess action accuracy, refine annotations, and maintain high-quality documentation following project rubrics and guidelines. This role emphasizes precise evaluation of movement and object interactions across video samples.

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