AI Training Data Annotator & Evaluator
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Annotated and evaluated AI training datasets in the STEM domain with consistent adherence to quality standards. Rated, labeled, and performed preference ranking of data, with a strong focus on accuracy and clarity across multilingual contexts. Evaluated AI-generated outputs for tone, cultural appropriateness, and fluency using linguistic and phonetic expertise.
• Applied phonetic and prosodic knowledge to assess suitability of speech data.
• Identified annotation errors, inconsistencies, and edge cases to improve dataset integrity.
• Recommended clear guidelines to refine labeling quality and reduce ambiguous interpretations.
• Worked with datasets in multiple languages, including underrepresented dialects.
Annotated and evaluated AI training datasets in the STEM domain with consistent adherence to quality standards. Rated, labeled, and performed preference ranking of data, with a strong focus on accuracy and clarity across multilingual contexts. Evaluated AI-generated outputs for tone, cultural appropriateness, and fluency using linguistic and phonetic expertise. • Applied phonetic and prosodic knowledge to assess suitability of speech data. • Identified annotation errors, inconsistencies, and edge cases to improve dataset integrity. • Recommended clear guidelines to refine labeling quality and reduce ambiguous interpretations. • Worked with datasets in multiple languages, including underrepresented dialects.
2023 - Present