Video Shot Boundary Annotator

You will analyze video clips in detail to create structured training data for AI systems. The work involves identifying where shots begin and end, describing transitions, organizing related shots, and applying content categories.

You will also keep labels consistent across high-volume work and flag unclear visual cases for review. Clear written notes are important when documenting ambiguous decisions.

• Watch video 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, or podcast and lifestyle content.
• Distinguish camera pans and zooms from actual shot changes.

What It Pays And Takes

Pay details are not provided for this role. The work is structured as part-time contract work and is open worldwide.

• Time requirement: 20 or more hours per week.
• Location: Open worldwide.
• Language: Professional fluency in English.
• Experience: At least one year in video or film editing, content creation, or video production.
• Knowledge: Strong understanding of shot composition, editing techniques, and transition types.
• Skills: Comfort with annotation tools and structured labeling workflows, independent work, close attention to detail, quality-focused execution, and clear written communication.
• Helpful background: Film studies, cinematography, media production, prior data labeling work, or familiarity with drama, documentary, gaming, podcast, and lifestyle genres.

How It Works

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

About AI Training Work

AI training work uses human judgment to prepare and review examples that help artificial intelligence systems learn. Video annotators contribute by turning visual content into accurate labels that models can use.