Robotic Video And Audiovisual Data Annotator

You will review robotic videos and related audiovisual content using structured project guidelines. Your work will help prepare accurate training data for AI systems.

You will judge both picture and sound, then record clear labels and concise feedback. Accuracy and consistency matter across every assigned task.

• Classify and label video segments using predefined categories.
• Identify actions, events, inconsistencies, errors, and unusual occurrences.
• Assess framing, continuity, synchronization, clarity, sound, and overall media quality.
• Describe reviewed content and provide concise written feedback.
• Incorporate feedback while maintaining consistent annotation quality.

This is a remote, part-time contractor opportunity for audiovisual professionals and contributors. Prior AI or data-annotation experience is not required if you bring strong audiovisual knowledge and careful judgment.

• Pay: USD 50 to USD 90 per hour.
• Time: 20 or more hours per week.
• Work arrangement:
  Remote contractor role.
• Language:
  Good written and verbal English communication skills.
• Location:
  
  Available in the countries listed for this role.
• Background:
  Professional or academic experience in audiovisual production, video production, filmmaking, media production, or a related field.
• Knowledge:
  Video and audio fundamentals, including framing, continuity, synchronization, and media quality.
• Skills:
  
  Precise observation, clear description of actions and events, attention to detail, and the ability to follow structured guidelines.
• Work style:
  Comfortable reviewing video for extended periods and working independently in a remote setting.

Experience with video editing, media review, quality control, post-production, structured audiovisual documentation, data annotation, video labeling, AI training, or dataset review can help. Familiarity with common video editing or audiovisual production tools is also useful.

AI training work is the human side of building artificial intelligence. People review and label videos, audio, text, and other data so AI systems can learn from accurate examples; experienced contributors are valued for their subject knowledge and careful judgment.