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 

Worldwide
Semi-flexible schedule
Fully-remote
full-time (40 hrs/week)
Long-term role

Content Annotator   $30,000 USD/year

Description

If accuracy matters more to you than speed, this position will be a natural fit. The labels you create become 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-powered learning experiences grounded in learning science, data analytics, and subject matter expertise. This position exists to convert raw student session videos into high-accuracy, rubric-based labels the team can rely on. You will review recorded student sessions, locate critical behavioral events, and apply rigorous rules to classify what occurred and at what timestamp. You will also evaluate LLM pre-annotations, correct errors, and document edge cases to help engineers refine the system.

This is not gig-based, random annotation work. It is a consistent queue within a single product domain, featuring direct feedback loops, calibration against gold standards, and advancement tied to accuracy and consistency. If you value clear expectations, measurable quality, and work that directly influences model performance, we would like to hear from you.

What you will be doing

  • Annotate student session videos by locating, classifying, and timestamping behavioral events according to a detailed rubric
  • Evaluate and correct LLM pre-annotations by eliminating false positives, inserting missed events, and refining timestamps
  • Document reasoning for non-obvious decisions, including rubric citations and the assumptions applied
  • Record edge cases and clarification questions for ambiguous scenarios and maintain an annotation tracker with session metadata
  • Participate in calibration exercises, incorporate QA feedback, and implement rubric updates to enhance accuracy over time

What you will NOT be doing

  • Develop AI models, conduct experiments, or perform research on student behavior
  • Create the annotation rubric or redefine category definitions based on personal interpretation
  • Prioritize speed over accuracy, consistency, or timestamp precision
  • Handle one-off, random tasks across unrelated domains without context or quality feedback

Key responsibilities

This role exists so that student session videos are transformed into ≥95%-accurate, time-precise labeled datasets that reliably indicate when model performance improves or regresses.

Candidate requirements

  • At least 1 year of experience in data annotation, content moderation, QA evaluation, or similar rubric-driven review work
  • Strong English reading comprehension and the ability to follow complex written instructions without deviating from the rules
  • Ability to maintain focus and accuracy for 4–6 hours of video-based work per day
  • Ability to detect subtle visual and on-screen behavioral cues and classify them consistently across many sessions
  • Strong written documentation skills for explaining edge cases, assumptions, and clarification questions
  • Reliable internet connection capable of streaming video
  • Comfort reviewing, correcting, and supplementing AI/LLM-generated annotations

Meet a successful candidate

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Fabiano Lucchese
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