Taylor Shin is an Applied Scientist with 11 years of experience building and deploying machine learning and AI systems across industry and academia, currently focused on sensitive content intelligence for Alexa at Amazon. With an MS in CS (AI & ML) from UC Irvine, Taylor has authored EMNLP research on probing NLP models and created gradient-based trigger search methods that improved factual knowledge extraction. He has production experience training and distilling Transformer models (including an 86% parameter reduction for GPT-2 parity on paraphrasing) and deploying ML services on AWS and Docker. Earlier roles span computer vision for biomedical imaging, music-generation research, and full-stack engineering for high-profile web launches like Overwatch League. Known for turning research ideas into production prototypes, he combines rigorous experimentation with practical system design and a knack for squeezing model efficiency without sacrificing accuracy.
Contributions:34 PRs, 36 pushes in 3 years 3 months
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