Video Scene Cut Annotation Specialist

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this project. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, connecting contributors with opportunities to help develop the systems shaping modern technology.

• Remote contract work open worldwide
• Free OpenTrain account and application process
• Opportunities to build experience in AI training and data labeling

About AI Training and Video Annotation

AI training is the human work behind artificial intelligence. People prepare and review examples, including labeled video, so AI systems can understand visual content and support tasks such as summarization, content analysis, and automated editing.

• Work with video data used in AI development
• Apply careful visual judgment to identify meaningful scene changes
• Contribute to a fast-growing, cutting-edge technology field

The Role

OpenTrain AI is seeking experienced video annotators or an annotation team for a temporal segmentation project covering approximately 1,000 YouTube videos. Videos average 5–10 minutes each, and the project is expected to run for 4–6 weeks.

You will identify every scene cut or transition and mark the exact start and end timestamp for each scene. The completed labels will support downstream video content analysis, summarization, and automated editing workflows.

• Contractor position
• Intermediate experience level
• Worldwide remote opportunity
• Pay: $4 USD per hour
• Preferred team size: 10–15 annotators
• Subject matter: media and entertainment video content editing

What You'll Do

You will annotate provided YouTube videos according to detailed project guidelines and examples. Each video must be segmented into scenes with accurate temporal boundaries and structured output fields.

• Annotate scene boundaries, including cuts and transitions
• Mark the start and end timestamp for every scene
• Review guidelines and examples before completing annotations
• Perform internal quality checks and revise labels before delivery
• Meet agreed project turnaround timelines
• Deliver JSON or CSV records containing video_id, scene_id, start_time, and end_time

Requirements and Quality Standards

Applicants should have prior experience with video annotation or temporal segmentation tasks. This project requires a reliable quality-control process and the ability to maintain accuracy across a large batch of videos.

• Prior video annotation or temporal segmentation experience
• Ability to follow detailed annotation instructions consistently
• Ability to complete internal QA checks and revisions
• Target accuracy of at least 95% on spot checks
• Ability to meet agreed turnaround timelines
• Readiness for weekly quality audits conducted by the internal QA team

Project Proposal Information

Teams and vendors should provide practical delivery details with their application. Individual annotators may also apply if they can demonstrate relevant experience and delivery capacity.

• Estimated team size
• Expected delivery capacity
• Cost per annotated video or per hour of video annotated
• Details about the internal QA process
• Approach to accuracy monitoring

Tools and Project Details

Access to the project annotation tool will be provided. The source project specifies uLabel for annotation, while the structured project details identify the environment as internal proprietary tooling.

• Data type: video
• Labeling type: temporal segmentation
• Approximately 1,000 videos
• Average video duration: 5–10 minutes
• Expected output: JSON or CSV
• Project duration: 4–6 weeks