AI Data Annotator & Search Engine Evaluator at Appen

Performed search engine evaluation (side-by-side tasks), comparing query results based on relevance, usefulness, and user intent. Annotated and evaluated multimodal datasets (text, image, audio, video) using structured guidelines to ensure accuracy, consistency, and quality. Assessed content quality, clarity, safety, and policy compliance across diverse data types. Maintained high accuracy and consistency (95%+ quality scores) across high volume tasks, and escalated ambiguous or borderline content for review. Provided clear written justifications for evaluation decisions, improving interpretability and dataset quality. Evaluated AI-generated outputs for relevance, clarity, and alignment with user intent using defined rating systems. Contributed to prompt-based evaluation workflows by analyzing task instructions and refining response quality.