Data 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 built for professionals who prioritize accuracy over speed. The annotations you produce become the training data that powers AI systems used daily by thousands of students. Precise labeling sharpens the model's intelligence; inconsistent labels teach it the wrong patterns.
LearnWith.AI creates AI-driven learning experiences grounded in learning science, data analytics, and expert knowledge. Your role is to convert raw video recordings of student sessions into high-fidelity, rubric-aligned labels the team can rely on. You will observe recorded sessions, pinpoint key behavioral moments, and apply rigorous classification rules to determine what occurred and when. You will also audit LLM-generated pre-annotations, correct errors, and flag edge cases to help engineers refine the system.
This is not random, gig-based annotation work. It involves a consistent task queue within one product area, supported by direct feedback channels, calibration against gold-standard examples, and advancement tied to accuracy and reliability. If you value transparent expectations, quantifiable quality standards, and contributions that directly influence model outcomes, we would like to hear from you.
What you will be doing
- Review and annotate student session videos by detecting, categorizing, and timestamping behavioral events according to a detailed rubric
- Audit and refine LLM pre-annotations by eliminating false positives, capturing missed events, and adjusting timestamps for precision
- Document reasoning for ambiguous decisions, citing rubric sections and outlining the assumptions applied
- Record edge cases and questions about unclear scenarios, and maintain an annotation tracker with session-level metadata
- Participate in calibration sessions, integrate QA feedback, and adapt to rubric revisions to enhance accuracy consistently
What you will NOT be doing
- Develop AI models, conduct experiments, or perform research into student behavior patterns
- Create or reinterpret the annotation rubric or modify category definitions based on personal judgment
- Prioritize speed over precision, consistency, or timestamp accuracy
- Handle scattered, one-time tasks across unrelated fields without context or feedback mechanisms
Key responsibilities
Your purpose in this role is to transform student session videos into labeled datasets with ≥95% accuracy and precise timestamps, ensuring the data reliably indicates whether model performance is advancing or declining.
Candidate requirements
- Minimum of 1 year of experience in data annotation, content moderation, QA evaluation, or comparable rubric-based review roles
- Excellent English reading comprehension with the capacity to adhere to detailed written guidelines without deviation
- Capability to maintain concentration and precision during 4–6 hours of video-based annotation work daily
- Skill in identifying subtle visual and on-screen behavioral signals and applying consistent classification across multiple sessions
- Solid written communication skills for articulating edge cases, assumptions, and requests for clarification
- Dependable internet connection suitable for video streaming
- Confidence in reviewing, correcting, and enhancing AI/LLM-produced annotations
Applying for a role? Here’s what to expect.
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Interview with the hiring manager.
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