Sumedh Pendurkar is a Research Scientist at Meta with nine years of experience focused on attention-based foundation models and recommendation systems. He brings a strong academic foundation from Texas A&M (PhD work) and a BTech from COEP, combined with hands-on internships that improved LLM fine-tuning memory usage and custom attention performance. His background spans teaching graduate machine learning and reinforcement learning courses, visiting research at University of Alberta, and applied ML roles at Niantic and Decompute, reflecting both theoretical depth and production-minded optimization. Based in College Station, Texas, he pairs research rigor with practical engineering wins—such as measurable speed and memory gains in attention layers—making him effective at bridging model innovation and system efficiency.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Technology (BTech), Computer Engineering, 9.12/10, Bachelor of Technology (BTech), Computer Engineering, 9.12/10 at College of Engineering Pune
Doctor of Philosophy - PhD, Computer Science, 4 / 4, Doctor of Philosophy - PhD, Computer Science, 4 / 4 at Texas A&M University
Contributions:13 commits, 11 pushes, 1 branch in 2 years 6 months
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