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 role offers meaningful work. The labels you create serve as training data for AI systems used daily by thousands of students. Precise behavioral labeling improves the product. Inconsistent labels teach the model incorrect patterns.
LearnWith.AI develops AI-driven learning experiences grounded in learning science, data analytics, and subject matter expertise. This position transforms raw student session videos into high-fidelity, rubric-aligned labels the team can rely on. You will review recorded student sessions, pinpoint critical behavioral events, and apply defined rules to classify actions and timing. You will also audit LLM pre-annotations, correct errors, and record edge cases to help engineers refine the system.
This is not ad hoc, gig-economy annotation work. It is a consistent workflow within one product domain, supported by direct feedback, calibration to gold standards, and advancement tied to accuracy and reliability. If you value transparent expectations, quantifiable quality standards, and work that directly shapes model outcomes, we should connect.
What you will be doing
- Label student session videos by detecting, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Audit and refine LLM pre-annotations by eliminating false positives, capturing overlooked events, and sharpening timestamp accuracy
- Document reasoning for ambiguous decisions, including rubric citations and the logic behind your choices
- Record edge cases and open questions for unclear scenarios, and maintain an annotation tracker with session details
- Participate in calibration sessions, integrate QA feedback, and adapt to rubric revisions to enhance precision over time
What you will NOT be doing
- Develop AI models, conduct experiments, or perform research on student behavior patterns
- Create the annotation rubric or reinterpret category definitions according to personal judgment
- Prioritize throughput over accuracy, consistency, or timestamp exactness
- Handle sporadic, disconnected tasks across unrelated subject areas with no feedback or quality assurance
Key responsibilities
This role ensures student session videos are transformed into ≥95%-accurate, temporally precise labeled datasets that dependably indicate whether model performance is advancing or declining.
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 detailed written instructions without deviation
- Capability to maintain concentration and precision during 4–6 hours of video-based work each day
- Skill in identifying subtle visual and on-screen behavioral signals and categorizing them uniformly across numerous sessions
- Proficient written documentation abilities for clarifying edge cases, reasoning, and open questions
- Dependable internet connection suitable for video streaming
- Confidence in reviewing, correcting, and enhancing AI/LLM-generated annotations
Applying for a role? Here’s what to expect.
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