Tim Gymnich
Software Engineer at AMD
Munich, Bavaria, Germany
Summary
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Rockstar🎓
Top SchoolTim Gymnich is a software engineer based in Munich with 10 years of experience specializing in programming languages, compilers, and developer tooling. He has a strong low-level systems background evidenced by contributions to LLVM, Swift’s tooling core, and Enzyme where he implemented concurrency primitives, read/write locks, and forward-mode automatic differentiation support. Tim blends practical engineering with rigorous testing—improving BigInt serialization and cross-platform test coverage—and has worked across industry teams including AMD and startups like Brium. A Technical University of Munich alumnus with time at MIT and UiT, he pairs academic depth with hands-on open-source impact on high-profile compiler projects. Notably, his work often sits at the intersection of concurrency, file-system correctness, and numerical compiler transforms, a niche that drives both performance and correctness.
10 years of coding experience
3 years of employment as a software developer
Computer Science, Computer Science at Massachusetts Institute of TechnologyComputer Science, Computer Science at UiT- The Arctic University of NorwayMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Technical University of Munich
German, English, French, Norwegian
Github Skills (37)
unit-testing10
algorithms10
filesystem10
c-language10
multithreading10
llvm10
testing10
locking10
bigint10
file-operations10
data-structure10
openmp10
automatic-differentiation10
low-level-programming10
code-generation10
Programming languages (23)
Github contributions (5)
EnzymeAD/Enzyme
Mar 2021 - Jan 2023
High-performance automatic differentiation of LLVM and MLIR.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:413 reviews, 324 commits, 378 PRs in 1 year 10 months
Contributions summary:Tim's contributions focused on implementing forward-mode automatic differentiation for the Enzyme library. This involved adding and modifying core components like the AdjointGenerator, and implementing a forward mode calling convention and unit tests. The user also refactored components, added support for various LLVM intrinsics, and addressed memory management to support the forward mode features. This indicates a focus on low-level, numerical, and compiler related aspects of automatic differentiation.
clangautomatic-differentiationsimulationcompilertensorflow
attaswift/BigInt
Mar 2020 - Dec 2022
Arbitrary-precision arithmetic in pure Swift
Role in this project:Back-end Developer & QA Engineer / Test Automation Engineer
Back-end Developer & QA Engineer / Test Automation EngineerContributions:12 releases, 13 reviews, 35 commits in 2 years 9 months
Contributions summary:Tim primarily focused on enhancing the functionality and reliability of the `bigint` library. Their contributions involved implementing and improving the initialization and serialization of `BigInt` and `BigUInt` types, including handling buffer and data conversions. They also added comprehensive unit tests and fixed existing ones to ensure the library's correctness across various scenarios and platforms, particularly Linux. Additionally, they updated the documentation.
arithmeticbignumarbitrary-precision-arithmeticbigintegerarbitrary
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