George Karpenkov - Staff Software Engineer at Tesla

George Karpenkov

Staff Software Engineer at Tesla

Mountain View, California, United States

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Summary

🤩

Rockstar

🎓

Top School

George Karpenkov is a Staff Software Engineer with 13 years of experience, based in Mountain View and currently working on Tesla's Dojo. At Google he led XLA:GPU performance, built a nine-person GPU performance team, and replaced direct-to-LLVM emitters with Triton to deliver 3–4x inference speedups and >2x gains for TensorFlow via tf.function(jit_compile=True). He is an OpenXLA maintainer and NVIDIA contact, with deep hands-on expertise in XLA/Triton, GPU code generation, and low-level compiler optimizations. His open-source footprint spans TensorFlow, JAX, LLVM/Clang, and even contributions to the Z3 Java API, reflecting a rare blend of compiler engineering, ML systems optimization, fuzzing, and formal-methods work. With PhD-level research at Université Grenoble Alpes and dual bachelors from the University of Sydney, he consistently turns low-level algorithmic improvements into multi-fold production speedups.

14 years of coding experience

12 years of employment as a software developer

Doctor of Philosophy (PhD), Doctor of Philosophy (PhD) at Université Grenoble Alpes

The University of Sydney

English, Russian, French

Stackoverflow

Stats

2,104reputation

216kreached

28answers

27questions

Github Skills (83)

python10

testing10

c1110

c1710

javas10

transformer-models10

code-generation10

gpu10

code-optimization10

javascript10

libfuzzer10

gpgpu10

object-oriented-programming10

ml10

verify10

Programming languages (15)

JavaC++CRustSWIGTeXGoJupyter Notebook

Github contributions (5)

apple/swift-clang

Aug 2017 - Feb 2019

Role in this project:

Back-end Developer

Contributions:461 commits, 4 PRs, 44 pushes in 1 year 6 months

Contributions summary:George contributed to the development of the Clang compiler, focusing on enhancements to the driver and related infrastructure. Their work included adding new functionality for fuzzing, such as the -fsanitize=fuzzer-no-link flag, and the integration of libFuzzer into the compiler-rt. They made modifications to the Darwin, Linux, and CommonArgs toolchains, as well as updates to the test suite, demonstrating expertise in compiler internals and sanitization techniques. Additional work also involved adding the support for OSObject based code, with tests that verify those aspects of the code.

openxla/xla

Mar 2019 - Jan 2023

A machine learning compiler for GPUs, CPUs, and ML accelerators

Role in this project:

Back-end Developer

Contributions:321 reviews, 600 commits, 1 PR in 3 years 11 months

Contributions summary:George primarily contributes to the XLA project's low-level code generation, focusing on implementing features and optimization around core mathematical operations like matrix multiplication and reductions. Their work involves performance improvements, refactoring of indexing logic, and implementing a tree-reduction strategy. They also address bugs in the existing implementations, particularly those related to layout handling. Their contributions show a deep understanding of the underlying mechanisms, and optimization considerations related to performance.

compilercommunity-drivenmachine-learningmodular

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