Data Annotation Specialist at iMerit (AI training dataset evaluation and labeling)
Served as a Data Annotation Specialist evaluating and labeling AI training datasets using structured annotation frameworks and detailed project rubrics. Assessed visual and media samples for quality, realism, accuracy, and compliance with defined evaluation standards while identifying edge cases and quality gaps. Escalated inconsistencies in AI-generated outputs with structured written justifications to improve dataset reliability and downstream model performance. • Labeled and reviewed AI training dataset samples using project rubrics • Evaluated media quality factors including realism, composition, lighting, and motion accuracy • Identified inconsistencies and edge cases, then provided documented escalation notes • Operated independently in a remote setting while meeting quality benchmarks