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Job Description
Structured overview of role & requirementsAbout This Role
Lead human evaluation and labeling of AI outputs to ensure product quality and benchmark performance.
Analyze and categorize failure modes such as hallucinations and retrieval failures to provide actionable feedback for cross-functional teams.
Maintain and improve dataset quality, calibration exercises, and quality reporting to support release readiness and model improvement.
Minimum Requirements
3–5 years of experience in data labeling, data analysis, QA, or a related field.
Experience evaluating AI-generated outputs or working with NLP, search, recommendation, or other ML systems.
Familiarity with qualitative analysis, basic SQL/data retrieval, and spreadsheet workflows.
Role requires hybrid work model with 4 days a week in-office attendance.
Ideal Candidate Profile
Strong analytical judgment with ability to apply complex rubrics consistently for AI output evaluation.
Experience working in ambiguous, fast-moving environments with cross-functional collaboration (QA, engineering, product, eval teams).
Skilled communicator capable of explaining technical and non-technical findings clearly across teams.