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
If you value correctness over speed and take pride in precision, this role will suit you well. The labels you produce become training data for AI systems used daily by thousands of students. Accurate behavioral annotation makes the product more intelligent. Inconsistent labels teach the model incorrect patterns.
LearnWith.AI creates AI-powered learning experiences grounded in learning science, data analytics, and subject matter expertise. This position transforms raw student session videos into high-quality, rubric-based labels the team can depend on. You will review recorded student sessions, pinpoint key behavioral events, and follow rigorous classification rules to determine what occurred and when. You will also evaluate LLM pre-annotations, correct errors, and document ambiguous cases so engineers can refine the system.
This is not gig-economy, task-hopping annotation. It is a focused queue within a single product domain, featuring direct feedback, calibration against gold-standard examples, and advancement tied to accuracy and consistency. If you seek well-defined expectations, quantifiable quality standards, and work that directly influences model performance, we would like to hear from you.
What you will be doing
- Annotate student session videos by pinpointing, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Evaluate and correct LLM pre-annotations by eliminating false positives, inserting missed events, and refining timestamp accuracy
- Document clear reasoning for ambiguous decisions, including relevant rubric sections and the assumptions applied
- Record edge cases and clarification requests 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 interpretation
- Prioritize speed over accuracy, consistency, or timestamp precision
- Handle ad-hoc, disconnected tasks across unrelated domains without context or quality feedback
Key responsibilities
This role ensures that student session videos are transformed into labeled datasets with ≥95% accuracy and time precision, reliably indicating when model performance advances or declines.
Candidate requirements
- At least 1 year of experience in data annotation, content moderation, QA evaluation, or comparable rubric-based review work
- Strong English reading comprehension with the capacity to follow detailed written instructions without deviating from established rules
- Ability to maintain focus and accuracy during 4–6 hours of video-based work per day
- Ability to detect subtle visual and on-screen behavioral signals and classify them uniformly across numerous sessions
- Strong written documentation skills for articulating edge cases, assumptions, and clarification requests
- Reliable internet connection suitable for streaming video
- Comfort with reviewing, 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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