Video Shot Boundary Annotator

The Work

You will review video clips frame by frame and create structured annotations for an AI training dataset. The work focuses on identifying where shots begin and end, recognizing transition types, and organizing related shots into scenes.

You will work across content such as drama, documentaries, gaming, podcasts, and lifestyle programming. Consistent decisions, accurate records, and clear notes on uncertain cases are important.

The listing describes this as an entry-level contractor role, but it requires at least one year of relevant video or film experience.

  • Identify every shot boundary in each video clip.
  • Record the start and end frame for every shot in a shot card.
  • Classify cuts, fades, dissolves, wipes, and related transition types.
  • Group shots into scene buckets based on narrative or event continuity.
  • Apply category labels to clips and scene buckets.
  • Distinguish camera movement from a true shot change.
  • Flag ambiguous visual cases and document edge cases or uncertain decisions.

What It Pays And Takes

The listing does not specify a pay rate. The role is structured as part-time contract work and requires at least 20 hours per week.

  • Pay: Not specified in the listing.
  • Schedule: Part time, 20+ hours per week.
  • Location: Worldwide.
  • Language: 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.
  • Work style: Comfortable using annotation tools and structured labeling workflows, working independently, and meeting quality and throughput targets.
  • Helpful background: Film studies, cinematography, media production, data annotation, or familiarity with drama, documentary, gaming, podcast, and lifestyle content.

How It Works

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

About AI Training Work

AI training work uses examples prepared and reviewed by people to help artificial intelligence systems learn. Video annotators mark useful details such as shot changes and scene relationships, and experienced reviewers help create consistent, reliable training data.

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