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
Not accepting applications on crossover.com at this time.
Description
If accuracy matters more to you than speed, this position is designed for you. The labels you create become the foundation for AI systems used by thousands of students daily. Precise behavioral labeling makes the product more intelligent. Inaccurate labels teach the model incorrect patterns.
LearnWith.AI develops AI-driven learning experiences through learning science, data analytics, and subject matter expertise. This position transforms raw student session recordings into high-precision, rubric-based labels the team relies on. You will observe 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 errors, and record edge cases to help engineers refine the system.
This is not gig-economy, ad-hoc annotation work. It involves a consistent workflow within a single product area, featuring direct feedback mechanisms, calibration against gold-standard examples, and advancement tied to accuracy and consistency. If you value clear standards, quantifiable quality metrics, and contributions that directly influence model performance, 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
- Evaluate and refine LLM pre-annotations by eliminating false positives, inserting overlooked events, and adjusting timestamps for precision
- Document reasoning for ambiguous decisions with clear notes, including rubric citations and the logic you applied
- Record edge cases and submit clarification requests for uncertain scenarios while maintaining an annotation tracker 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 on student behavior patterns
- Create the annotation rubric or alter category definitions based on subjective interpretation
- Prioritize speed over accuracy, consistency, or precise timestamping
- Handle sporadic, unrelated tasks across disparate domains without context or quality feedback mechanisms
Key responsibilities
This position ensures that student session recordings are transformed into ≥95%-accurate, temporally precise labeled datasets that reliably indicate when model performance advances or declines.
Candidate requirements
- Minimum 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 instructions without deviating from established rules
- Capacity to maintain concentration and precision during 4–6 hours of video-focused work daily
- Skill in identifying subtle visual and on-screen behavioral signals and classifying them uniformly across numerous sessions
- Proficient written documentation abilities for articulating edge cases, reasoning, and clarification inquiries
- Stable internet connection suitable for video streaming
- Experience reviewing, 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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