Video Annotator $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
Description
This role is for professionals who value correctness over speed. The labels you create become the foundation for AI models used by thousands of students daily. Accurate behavioral labeling improves the product. Inconsistent labeling teaches the model incorrect patterns.
LearnWith.AI develops AI-driven learning platforms through learning science, data analysis, and expert collaboration. Your responsibility is to transform unprocessed student session recordings into precise, rubric-based labels the team can depend on. You will review recorded student interactions, pinpoint critical behavioral moments, and follow rigorous classification protocols to document what occurred and its timing. You will also audit LLM-generated pre-annotations, correct inaccuracies, and record edge cases to help engineers refine the system.
This is not freelance, fragmented annotation work. It involves a consistent workflow within a focused product area, featuring direct feedback mechanisms, validation against gold-standard benchmarks, and advancement tied to precision and reliability. If you seek transparent expectations, quantifiable quality standards, and assignments that directly influence model outcomes, we would like to hear from you.
What you will be doing
- Label student session recordings by detecting, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Audit and refine LLM pre-annotations by eliminating false positives, capturing overlooked events, and improving timestamp accuracy
- Document clear rationale for complex decisions, citing rubric sections and the reasoning frameworks applied
- Record edge cases and ambiguity-related questions for uncertain scenarios, and maintain an annotation log with session details
- Participate in calibration sessions, integrate QA feedback, and implement rubric revisions to enhance accuracy progressively
What you will NOT be doing
- Develop AI models, conduct experiments, or perform research into student behavioral patterns
- Create the annotation rubric or reinterpret category definitions based on subjective judgment
- Prioritize speed over accuracy, consistency, or timestamp exactness
- Handle sporadic, disconnected tasks across disparate domains without context or feedback systems
Key responsibilities
Your purpose is to ensure student session recordings are transformed into labeled datasets with ≥95% accuracy and precise timestamps, enabling reliable evaluation of model performance improvements or declines.
Candidate requirements
- A minimum of 1 year in data annotation, content moderation, QA assessment, or comparable rubric-based review roles
- Excellent English comprehension and the capacity to adhere to detailed written guidelines without deviation
- Capability to maintain concentration and precision during 4–6 hours of video-based tasks daily
- Skill in identifying subtle visual and on-screen behavioral signals and applying consistent classification across numerous sessions
- Proficient written documentation abilities for articulating edge cases, reasoning, and questions requiring clarification
- Dependable internet connection suitable for video streaming
- Experience reviewing, correcting, and enhancing AI/LLM-produced annotations
Applying for a role? Here’s what to expect.
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