Kumud Bhandari
Compiler Engineer, XLA TPU Optimizations
Houston, Texas, United States
Summary
👤
Senior🎓
Top SchoolKumud Bhandari is a compiler engineer with 10 years of experience optimizing machine learning workloads for emerging accelerators, currently making Gemini and other generative models run faster on Google TPUs. With a PhD from Rice University and a track record at Facebook AI (PyTorch Glow) and Xilinx, he specializes in backend code generation, IR-to-hardware compiler passes, and heterogeneous runtime systems. He has built modular simulation and verification frameworks for ACAP-class accelerators and shipped production compiler optimizations for novel ML hardware. Comfortable moving between research and production, Kumud blends deep academic rigor with practical engineering to squeeze performance from next-generation silicon. An uncommon asset is his breadth across both hardware-aware simulation and open-source ML compiler projects, enabling rapid delivery of optimized model execution.
10 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Rice UniversityBachelors of Science Bachelors of Arts Computers Science Mathematics, Bachelors of Science Bachelors of Arts Computers Science Mathematics at McKendree University
Github Skills (8)
hardware9
atlas9
persistent8
persistent-memory8
java7
neural-network6
compiler6
cpp6
Programming languages (1)
Github contributions (3)
kumudb/glow
Sep 2019 - Sep 2019
Compiler for Neural Network hardware accelerators
Contributions:2 PRs, 12 pushes in 2 days
hardware-acceleratorscompilerneural-networkacceleratorshardware
HewlettPackard/Atlas
May 2016 - Aug 2016
Atlas: Programming for Persistent Memory
Contributions:47 commits in 3 months
persistent-memorymemorycppatlaspersistent
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