Content 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
If accuracy matters more to you than speed, this position will suit you well. The work you produce serves as training data for AI systems used daily by thousands of students. Precise 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 converts raw student session videos into high-accuracy, rubric-based labels the team can depend on. You will review recorded student sessions, recognize critical behavioral events, and follow rigorous protocols to classify what occurred and when. You will also audit LLM pre-annotations, correct inaccuracies, and record edge cases so engineers can refine the system.
This is not gig-economy, ad-hoc annotation work. It involves a consistent queue within a single product area, with immediate feedback mechanisms, calibration against gold-standard benchmarks, and advancement tied to accuracy and consistency. If you value transparent expectations, quantifiable quality standards, and contributions that directly affect model performance, we would like to hear from you.
What you will be doing
- Label student session videos by recognizing, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Audit and refine LLM pre-annotations by eliminating false positives, inserting overlooked events, and sharpening timestamp accuracy
- Document clear reasoning for ambiguous decisions, citing rubric sections and the logic you applied
- Record edge cases and questions requiring clarification for unclear 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
- Develop AI models, conduct experiments, or perform research on student behavior patterns
- Create the annotation rubric or alter category definitions based on subjective judgment
- Prioritize speed over accuracy, consistency, or timestamp precision
- Handle sporadic, disconnected tasks across unrelated areas with no context or quality feedback
Key responsibilities
This position ensures that student session videos are transformed into ≥95%-accurate, temporally precise labeled datasets that dependably indicate when model performance advances or declines.
Candidate requirements
- At least 1 year of experience in data annotation, content moderation, QA evaluation, or similar rubric-based review work
- Strong English reading comprehension and the capacity to adhere to complex written instructions without deviating from established rules
- Capacity to maintain focus and precision during 4–6 hours of video-based work per day
- Ability to detect subtle visual and on-screen behavioral signals and categorize them uniformly across multiple sessions
- Strong written documentation abilities for clarifying edge cases, assumptions, and questions requiring clarification
- Reliable internet connection suitable for streaming video
- Comfort auditing, correcting, and enhancing AI/LLM-generated annotations
Applying for a role? Here’s what to expect.
Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.
Chat-style
screening interview.
Cognitive 
aptitude test.
Prove real-world 
job skills.
Interview with the hiring manager.
Pass
proctored test.
Accept job offer.
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