Adam Paszke
Senior Staff Research Scientist at Google DeepMind
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolAdam Paszke is a Senior Staff Research Scientist at Google DeepMind and the original author of PyTorch, with 11 years of experience building machine learning tooling and models. He combines deep research on automatic differentiation and ML compilers with hands-on low-level systems work—C/CUDA optimizations, FP16 support, memory and file-handling fixes, and XLA/JAX internals. An active open-source maintainer, his contributions span PyTorch examples and core backends, Torch7, hasktorch and LLVM-HS, reflecting fluency across Python, C/CUDA and Haskell ecosystems. Based in Warsaw and trained in both computer science and mathematics at the University of Warsaw, he’s known for translating compiler-level ideas into production-ready deep learning infrastructure.
11 years of coding experience
5 years of employment as a software developer
Uniwersytet Warszawski
English, German
Github Skills (73)
float3210
python10
operation10
c1110
c1710
lua10
deep-learning10
file-processing10
floating-point10
haskell10
memory-management10
api10
xla10
compiler10
computer-vision10
Programming languages (16)
Github contributions (5)
google-research/dex-lang
Sep 2019 - Jan 2023
Research language for array processing in the Haskell/ML family
Role in this project:
Backend Developer
Backend DeveloperContributions:870 reviews, 852 commits, 699 PRs in 3 years 4 months
Contributions summary:Adam contributed to the research language for array processing in the Haskell/ML family. The commits focused on refactoring, modifying the parser for core language features, and adding built-in features like the `FromInteger` interface with associated type, and functions for array manipulation, by implementing primitives and built-in functions to the core. The changes also included optimizations and improvements to the underlying LLVM-based compiler, with particular focus on enabling floating point operations, custom linearizations.
arrayhomotopy-type-theoryautomatic-differentiationhaskellarray-processing
llvm-hs/llvm-hs
Dec 2020 - Oct 2022
Haskell bindings for LLVM
Role in this project:Back-end Developer
Back-end DeveloperContributions:7 reviews, 43 commits, 39 PRs in 1 year 9 months
Contributions summary:Adam primarily focused on enhancing the Haskell bindings for LLVM. Their contributions involved modifying the LLVM-HS library's internal modules to allow for non-bracketed management of contexts and modules, useful when the compilation process is driven from outside of Haskell. The user also removed OrcJITv1, with associated cleanups and fixes, and added support for object linking layers. Furthermore, they worked on enabling absolute symbols in JITDylibs and exposing function attributes in the AST.
llvm-ircode-generationllvm-hshaskell-bindingshaskell
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