Data Labeler

$30,000 USD/year ^{Pay is set based on global value, not the local market. Most roles = hourly rate x 40 hrs x 50 weeks}  ($15 USD/hour)

• Annotate student session videos by identifying, classifying, and timestamping behavioral events according to a detailed rubric
• Audit and correct LLM pre-annotations by eliminating false positives, capturing missed events, and refining timestamps
• Document clear reasoning notes for ambiguous decisions, including rubric citations and the assumptions applied
• Record edge cases and clarification questions for uncertain scenarios and maintain an annotation tracker with session metadata
• Participate in calibration exercises, integrate QA feedback, and apply rubric revisions to enhance accuracy continuously

• Construct AI models, conduct experiments, or perform research on student behavior
• Design the annotation rubric or redefine category definitions according to personal interpretation
• Prioritize speed over accuracy, consistency, or timestamp precision
• Handle one-off, scattered tasks across unrelated domains without context or quality feedback

This role ensures that student session videos are converted into ≥95%-accurate, time-precise labeled datasets that reliably indicate when model performance improves or regresses.

• At least 1 year of experience in data annotation, content moderation, QA evaluation, or similar rubric-driven review work
• Strong English reading comprehension and the ability to follow complex written instructions without deviating from the rules
• Ability to maintain focus and accuracy for 4–6 hours of video-based work per day
• Ability to detect subtle visual and on-screen behavioral cues and classify them consistently across many sessions
• Strong written documentation skills for explaining edge cases, assumptions, and clarification questions
• Reliable internet connection capable of streaming video
• Comfort reviewing, correcting, and supplementing AI/LLM-generated annotations