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AI Data Annotation Expert | $40-$80/hr Remote

40–80/hr
Remote
Posted August 14, 2026
contract
100 openings

Overview

A remote contractor role focused on high-precision video data labeling to support AI training. You’ll translate real-world video moments into structured annotations that guide model learning and behavior. The work values domain knowledge and careful, guideline-driven labeling, with no AI background required beyond your expertise in the subject area.

What You'll Do7

  • 1Watch short video clips and detect common events guided by the provided labeling rules
  • 2Mark exact timestamps for each identified event with precision
  • 3Write clear, concise descriptions for every annotated event
  • 4Maintain high data quality by applying strict annotation standards and accuracy checks
  • 5Follow established protocols to ensure consistency across the labeled dataset
  • 6Collaborate with project leads and teammates to resolve questions and incorporate feedback
  • 7Deliver outputs on schedule, aligned with project milestones

Requirements4

  • 1Experience in video annotation or similar data labeling work
  • 2Exceptional attention to detail and commitment to data quality
  • 3Strong analytical ability to interpret guidelines and apply them consistently
  • 4Proficient written and spoken English for clear descriptions and communication

Who Should Apply

Ideal candidates bring seasoned accuracy in labeling tasks, thrive in remote, collaborative settings, and can adapt to evolving criteria. A background in AI training or data-focused projects is a plus, but domain knowledge in the subject matter matters most.

Salary Insight

Compensation ranges from $40 to $80 per hour for contract work.

Location

TypeRemote
LocationRemote
This is a remote position

Required Skills

Video AnnotationAttention to detailData Labeling AccuracyEnglish-language ProficiencyAI trainingCollaborative MindsetAnalytical ThinkingAbility to follow guidelines

Application Tip

Highlight a past video labeling project where you achieved high consistency metrics and describe how you handled guideline changes mid-project.

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