Shikib Mehri is an applied scientist based in San Jose with 11 years of experience translating cutting-edge NLP research into production products, currently leading reward modeling, evaluation, and data collection efforts for LLM features at Amazon. A Carnegie Mellon PhD student focused on dialog systems, he has a strong track record across industry research internships and roles at Facebook, Microsoft, and Amazon, and academic work on conversational thread disentanglement and graph-based read alignment. He independently built initial reward model pipelines and evaluation methodologies that shaped program-level practices, and has run end-to-end annotation efforts aligning data collection with product requirements. Comfortable moving between research and engineering, he combines peer-reviewed publications with measurable product impact (e.g., shipped MT improvements and conversational ASR work), reflecting a pragmatic researcher who ships.
11 years of coding experience
4 years of employment as a software developer
The University Transition Program
Bachelor of Science (BSc), Honours Computer Science, Senior Year, Bachelor of Science (BSc), Honours Computer Science, Senior Year at The University of British Columbia
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Carnegie Mellon University
Code for the SIGDial 2021 paper: Schema-Guided Paradigm for Zero-Shot Dialog
Contributions:4 commits, 2 PRs, 2 pushes in 2 months
dialogzerozero-shotparadigmsigdial
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