Description:
If you value precision over speed, this position offers the focus you need. The labels you produce serve as training data for AI systems used daily by thousands of students. Accurate behavioral tagging makes the product more intelligent. Label inconsistency teaches the model incorrect patterns.
LearnWith.AI creates AI-powered educational 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 can depend on. You will review recorded student sessions, pinpoint critical behavioral events, and apply rigorous criteria to classify actions and timing. You will also assess LLM pre-annotations, correct inaccuracies, and record edge cases to help engineers refine the system.
This is not freelance-style, scattered task annotation. It is a consistent workflow within one product domain, featuring direct feedback mechanisms, calibration to gold standards, and advancement tied to precision and reliability. If you seek well-defined expectations, quantifiable quality metrics, and work that directly influences model accuracy, we should connect.
What You Will Be Doing
- Label student session recordings by locating, categorizing, and timestamping behavioral events according to a comprehensive rubric
- Evaluate 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 clarification requests for uncertain scenarios, and maintain an annotation log with session details
- Participate in calibration sessions, integrate QA feedback, and implement rubric revisions to enhance precision over time
What You Won’t Be Doing
- Develop AI models, conduct experiments, or perform research into student behavior patterns
- Create the annotation rubric or alter category meanings based on subjective interpretation
- Prioritize throughput over accuracy, reliability, or timestamp exactness
- Handle sporadic, disconnected tasks across unrelated fields with no background or quality feedback
Data Labeler Key Responsibilities
This position ensures student session recordings are transformed into ≥95%-accurate, temporally precise labeled datasets that dependably indicate when model performance advances or declines.
Basic Requirements
- Minimum 1 year of experience in data annotation, content moderation, QA evaluation, or comparable rubric-based review roles
- Excellent English reading comprehension and capacity to adhere to detailed written guidelines without deviation
- Capacity to maintain concentration and precision during 4–6 hours of daily video-based tasks
- Skill in detecting subtle visual and on-screen behavioral signals and categorizing them uniformly across multiple sessions
- Solid written communication skills for articulating edge cases, reasoning, and clarification inquiries
- Stable internet connection suitable for video streaming
- Ease with reviewing, correcting, and enhancing AI/LLM-produced annotations
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