Srini Iyer
Staff Research Scientist at Meta
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolSrini Iyer is a Staff Research Scientist at Meta FAIR in Seattle with a decade of experience bridging machine learning research and production systems. He earned a PhD in Computer Science from the University of Washington after an MS from Stanford, and his early career included applied engineering roles on Facebook’s Ads Ranking team and backend infrastructure. At Meta he focuses on large language models, combining deep academic rigor with practical expertise in feature engineering, CTR/conversion prediction, and ads retrieval optimization. His background spans from building remote-server automation tools at Microsoft to low-level data infrastructure work as a Facebook intern, highlighting a strong systems-to-research trajectory. Known for translating research advances into scalable products, he brings both experimental depth and hands-on engineering to production ML challenges.
9 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of WashingtonBachelor of Engineering (B.E.), Computer Engineering, Bachelor of Engineering (B.E.), Computer Engineering at VESIT, Mumbai, IndiaMaster of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Stanford University
English, Hindi
Github Skills (66)
huggingface10
scale10
attention-model10
nlp9
transformers9
python9
neural-machine-translation9
pytorch9
docker9
large-scale9
natural-language-processing9
bit9
transformer8
utilization8
multi-gpu8
Programming languages (1)
Github contributions (5)
sriniiyer/concode
Sep 2018 - Jan 2021
Mapping Language to Code in a Programmatic Context
Contributions:11 commits, 2 PRs, 3 pushes in 2 years 4 months
mappingprogrammatic
sriniiyer/codenn
Jun 2016 - May 2017
Summarizing Source Code using a Neural Attention Model - CODENN
Contributions:12 commits, 1 PR, 13 pushes in 11 months
pytorchnlpnatural-language-processingattention-model
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