Gleb Kazantaev
Member Of Technical Staff at Cerebras Systems
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolGleb Kazantaev is an AI frameworks engineer with a decade of experience building and optimizing deep learning compilers and runtimes, currently a Member of Technical Staff at Cerebras Systems. He has led performance-critical C++ and Python development at Intel, architecting graph optimization techniques, inference runtimes, and model conversion tooling across TensorFlow, Caffe, and PyTorch. His open-source contributions to llvm/torch-mlir reflect deep expertise in bridging PyTorch and MLIR ecosystems, improving backend contracts and type/import handling for LTC. Comfortable across the full development lifecycle and cross-geo projects, he pairs low-level systems work with higher-level graph transformations. Outside engineering he runs a local music studio and shoots aero-photography, bringing a creative, detail-oriented perspective to problem solving.
10 years of coding experience
6 years of employment as a software developer
Master's degree, Information Technology, Master's degree, Information Technology at Nizhny Novgorod State Technical University n.a. R.E. Alekseev
Github Skills (12)
mle10
pytorch10
c-language10
backend10
cprogramming-language10
back-end-development10
python10
mlr10
ml10
compiler-compiler9
compiler9
testing8
Programming languages (3)
Github contributions (5)
llvm/torch-mlir
Oct 2022 - Jan 2023
The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:27 reviews, 9 commits, 26 PRs in 3 months
Contributions summary:Gleb primarily focused on enhancing the Torch-MLIR project, specializing in the integration of PyTorch and MLIR ecosystems. Their contributions involved significant changes to the backend implementation, specifically related to type conversions and the import of IValues. The user also worked on supporting new operations and improving the backend contract for the LTC (Lazy Tensor Core) backend. Additionally, they addressed issues related to schema matching and enabled the VerifyBackendContract pass within the LTC backend, demonstrating a deep understanding of the project's internal workings.
pytorchmlirtorchcompilerecosystem
GlebKazantaev/openvino
May 2020 - May 2023
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
Contributions:4 PRs, 825 pushes, 221 branches in 3 years
pytorchdeep-learningdeploymentopenvino-toolkitinference
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