Video Annotator (English) for Shot Boundaries

You will analyze video clips in detail to create structured training data. The work combines frame-level visual judgment with narrative and genre-based organization of video content.

You will maintain consistent, accurate labels across a high volume of clips and flag unclear visual cases for review.

• Watch clips frame by frame and identify every shot boundary.
• Create a shot card showing the beginning and ending frame for each shot.
• Classify transition types and subtypes, including cuts, fades, dissolves, and wipes.
• Group related shots into scene buckets based on narrative or event continuity.
• Assign categories such as television and streaming drama, education and documentary, gaming and science fiction, and podcast and lifestyle content.
• Document ambiguous decisions and follow structured labeling guidelines.

The role is listed as part-time contract work. No pay rate is provided in the role details.

• Time requirement: 20+ hours per week.
• Location: Open worldwide.
• Language: Fluent in English.
• Experience: At least one year in video or film editing, content creation, or video production.
• Skills: Strong knowledge of shot composition, editing techniques, and transition types.
• You must distinguish camera pans and zooms from actual shot changes.
• You should be comfortable with annotation tools and structured labeling workflows.
• You need independent work habits, close attention to detail, quality-focused execution, and clear written communication.
• Helpful background includes film studies, cinematography, media production, data annotation, and familiarity with drama, documentary, gaming, podcast, or lifestyle genres.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training work uses examples prepared and reviewed by people to help artificial intelligence systems learn. Video annotators support this process by turning visual content into consistent labels that models can use.