Matthew Brookhart is a Principal Engineer with a PhD in Physics and nine years of experience transforming applied mathematics into production-grade AI and compiler software. He has led teams and shipped deep learning compiler and runtime work at Intel, OctoML, Modular, and now NVIDIA, specializing in bridging research-grade models to optimized, heterogeneous hardware execution. His background in computational lithography and experimental plasma diagnostics gives him an uncommon blend of precision instrumentation, predictive modeling, and systems-level engineering. Known for turning complex math into efficient code, he favors principled engineering and will not compromise on ethics or forced-arbitration contracts. Based in Salt Lake City, he is a physicist-turned-ML engineer who thrives on hard problems that sit at the intersection of algorithms, compilers, and hardware.
9 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of Wisconsin-Madison
Bachelor of Science (B.S.) Double Major in Physics and Applied Mathematics, Bachelor of Science (B.S.) Double Major in Physics and Applied Mathematics at University of Idaho
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:7 PRs, 66 pushes, 6 branches in 7 months
pythonschedulerfeature-storedataflowmutation
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