Yu-hsiang Lin is a software engineer with 11 years of experience building production-grade ML systems, currently at Meta after five years as an Applied Scientist at Amazon. He specializes in large language models and NLP—leading work on instruction-following LLMs, RLHF (PPO, RM, DPO), RAG, self-instruct/self-revise data generation, and human preference evaluation for Alexa. Earlier roles in Amazon Search and A9 involved neural generative models, graph neural networks for low-resource product search, two-phase ranking, and production data pipelines powering A/B metrics and index publishing. He combines deep research training (PhD in Physics) with practical CS mastery from Carnegie Mellon, enabling both novel modeling and scalable deployment. Known for pragmatic prompt engineering and curriculum learning approaches, he often bridges research prototypes and production constraints. Based in Cambridge, MA, he keeps an active project portfolio and website that surfaces his latest experiments and publications.
11 years of coding experience
6 years of employment as a software developer
Ph.D. Physics, Ph.D. Physics at National Taiwan University
Master Computer Science, Master Computer Science at Carnegie Mellon University
Contributions:35 commits, 3 PRs, 51 pushes in 2 years
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