Maksim Levental
Senior ML Infrastructure Engineer at Apple
Cupertino, California, United States
Summary
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Rockstar🎓
Top SchoolMaksim Levental is a Senior ML Infrastructure Engineer in Boston with 11 years of experience building compilers, ML runtimes, and accelerator backends. He specializes in DSLs, compilers, accelerator architectures, and applied math, and contributes to high-profile open-source projects like torch-mlir (eager-mode backend), Triton (AMD GPU codegen and FP8 matmul support), and LLVM/MLIR Python bindings. At Apple he works on CoreML + MLIR, and prior roles at AMD involved compiler research and Triton/IREE work for Instinct/CDNA and Xilinx AI Engines, while earlier positions at Meta/Facebook focused on PyTorch pipeline parallelism and JIT memory optimizations. Known for bridging research and production, he turns MLIR prototypes into eager GPU execution and performance-focused compiler integrations that improve real-world model throughput. He combines advanced computer science training and a BS in mathematics with an unusual background in communication—having served as a Peace Corps secondary school teacher early in his career.
12 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BS), Mathematics, 3.73, Bachelor of Science (BS), Mathematics, 3.73 at Florida State UniversityMaster of Science - MS, Computer Science, 4.0, Master of Science - MS, Computer Science, 4.0 at University of ChicagoMaster’s Degree, Computer Science, 3.72, Master’s Degree, Computer Science, 3.72 at University of Florida
English, Russian
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (31)
pytorch10
python10
back-end-development10
llvm10
matrix-multiplication10
amd-gpu10
machine-learning10
mlr10
nvidia10
p810
triton10
compiler-compiler10
performance-analysis10
eager-execution10
compiler-optimization10
Programming languages (34)
Github contributions (5)
llvm/torch-mlir
Mar 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
Back-end DeveloperContributions:118 reviews, 19 commits, 87 PRs in 10 months
Contributions summary:Maksim primarily focuses on extending the `torch-mlir` framework to support eager mode execution for PyTorch. They implemented an eager mode backend, enabling the compilation of PyTorch operations on-the-fly. The user also addresses bugs, refactors code, and enhances the framework with features like the decomposition of certain operations. They also added examples and made updates for the use of the eager mode.
pytorchmlirtorchcompilerecosystem
llvm/llvm-project
Aug 2022 - Apr 2025
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Role in this project:Back-end Developer
Back-end DeveloperContributions:344 reviews, 213 PRs, 138 pushes in 2 years 7 months
Contributions summary:Maksim primarily contributed to the Python bindings for the LLVM MLIR project. Their work involved adding and modifying Python interfaces for various MLIR features, specifically the GPU dialect, LLVM dialect, and the `linalg` dialect. They implemented attribute and type casting for new classes and improved the behavior of existing functionalities such as the handling of `memref` objects. The user's changes also included adjustments to testing frameworks, test cases, and the inclusion of support for the `CLANG_CL` compiler.
compilerstechnologiesclangsubmittoolchain
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