Annotation Specialist $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
If accuracy matters more to you than speed, this position will suit you well. The labels you create serve as training data for AI systems supporting thousands of students daily. Precise behavioral labeling makes the product more intelligent. Inconsistent labels teach the model incorrect patterns.
LearnWith.AI creates AI-driven learning experiences through learning science, data analytics, and subject matter expertise. This position transforms raw student session recordings into high-accuracy, rubric-aligned labels the team relies on. You will review recorded student sessions, pinpoint critical behavioral events, and apply rigorous classification rules to determine what occurred and when. You will also evaluate LLM pre-annotations, correct inaccuracies, and record edge cases to help engineers enhance the system.
This is not freelance-style, fragmented annotation work. It involves a consistent queue within one product domain, featuring direct feedback mechanisms, calibration to gold standards, and advancement tied to accuracy and consistency. If you value well-defined expectations, quantifiable quality standards, and work that influences model performance directly, we would like to hear from you.
What you will be doing
- Label student session recordings by identifying, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Evaluate and correct LLM pre-annotations by eliminating false positives, inserting overlooked events, and refining timestamps
- Document clear reasoning for ambiguous judgments, including rubric citations and the assumptions applied
- Record edge cases and clarification inquiries for uncertain scenarios and maintain an annotation tracker with session metadata
- Participate in calibration exercises, integrate QA feedback, and implement rubric revisions to enhance accuracy progressively
What you will NOT be doing
- Construct 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 fields with no context or quality feedback
Key responsibilities
This position ensures that student session recordings are transformed into ≥95%-accurate, time-precise labeled datasets that dependably indicate when model performance advances or declines.
Candidate requirements
- A minimum of 1 year of experience in data annotation, content moderation, QA evaluation, or comparable rubric-based review work
- Excellent English reading comprehension and the capacity to adhere to complex written instructions without deviating from the rules
- Capacity to maintain focus and accuracy 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 clarification inquiries
- Dependable internet connection suitable for streaming video
- Comfort evaluating, correcting, and enhancing AI/LLM-generated annotations
Applying for a role? Here’s what to expect.
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Prove real-world 
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Interview with the hiring manager.
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