AI Content Relevance Evaluator
Evaluated image-to-image relevance for an AI recommendation system by assessing semantic and visual alignment according to structured relevance guidelines.
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I have direct experience transcribing, translating, and labeling structured data for language models in AI training data projects. Specifically, I focused on segmenting audio and video content and applying accurate labels to generate high-quality bilingual output in both English and Korean. This work required adherence to detailed annotation guidelines, consistent formatting, and a high level of linguistic accuracy, ensuring data suitable for AI system training and evaluation. I also have experience reviewing and validating labeled data for quality, accuracy, and consistency. This experience has developed the ability to identify subtle errors in segmentation, timing, terminology, and semantic preservation. I am particularly strong in attention to detail, independent quality control, and maintaining reliability across large datasets. This background allows me to effectively contribute to human-in-the-loop AI workflows, where accuracy and consistency are crucial.
Evaluated image-to-image relevance for an AI recommendation system by assessing semantic and visual alignment according to structured relevance guidelines.
LLM response evaluation and rating.
Recorded audio according to a script, adhering to strict linguistic and technical guidelines, and generated high-quality data for large-scale language and speech recognition models.
Segment and then translate video/audio.
Bachelor of Science, Computer Science
Certificate of Qualification, Assistant of Maintenance PC Network
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