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)

• Label student session recordings by locating, categorizing, and timestamping behavioral events according to a comprehensive rubric
• Audit and refine LLM pre-annotations by eliminating false positives, capturing overlooked events, and sharpening timestamp accuracy
• Document clear reasoning for ambiguous decisions, referencing rubric guidelines and the assumptions behind your classification
• Record edge cases and clarification requests for uncertain scenarios, and maintain an annotation tracker with session metadata
• Participate in calibration sessions, incorporate QA feedback, and adapt to rubric revisions to enhance accuracy continuously

• Develop AI models, conduct experiments, or perform research on student behavior patterns
• Create the annotation rubric or reinterpret category definitions according to personal judgment
• Prioritize speed over accuracy, consistency, or timestamp precision
• Handle sporadic, disconnected tasks across unrelated domains without context or quality feedback

This position ensures student session recordings are transformed into ≥95%-accurate, time-precise labeled datasets that reliably indicate when model performance advances or declines.

• Minimum 1 year of experience in data annotation, content moderation, QA evaluation, or comparable rubric-based review roles
• Excellent English reading comprehension and capacity to adhere to complex written guidelines without deviation
• Capacity to maintain focus and precision during 4–6 hours of video-based work daily
• Ability to detect subtle visual and on-screen behavioral signals and classify them uniformly across numerous sessions
• Excellent written documentation abilities for clarifying edge cases, assumptions, and questions
• Dependable internet connection suitable for video streaming
• Comfort reviewing, correcting, and enhancing AI/LLM-produced annotations