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Mid-level AI data role in metro at a visible startup with common skillset increases applicant competition.
AI evaluation and labeling skills are transferable, but preference for ML product experience raises sensitivity.
Explicit 3–5 years plus AI evaluation and SQL expectations impose moderate shortlisting filters.
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Manage and run ongoing human labeling and evaluation of AI outputs for quality assurance and product improvement.
Analyze and categorize AI failure modes, maintain high-quality labeled datasets, and support benchmark updates.
Collaborate cross-functionally with QA, Engineering, Product, and evaluation teams to turn ambiguous quality issues into actionable feedback and quality signals.
3-5 years of experience in data labeling, data analysis, QA, or related field.
Experience evaluating AI-generated outputs or working with NLP, search, recommendation, or ML systems.
Familiarity with structured qualitative analysis, basic SQL/data retrieval, and spreadsheet workflows.
Role location: Hybrid with 4 days per week in-office attendance.
Analytical professional skilled in nuanced rubric application and detailed qualitative analysis of AI system failures.
Experienced in cross-functional collaboration in fast-paced environments with ambiguous inputs.
Proficient communicator able to explain technical findings to both technical and non-technical audiences.