Ludger Paehler
Machine Learning Engineer at Technical University of Munich
San Francisco, California, United States
Summary
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RockstarLudger Paehler is a Machine Learning Engineer based in San Francisco with nine years of experience specializing in differentiable programming and high-performance automatic differentiation. He contributes to critical open-source infrastructure—most notably optimizing Enzyme for LLVM and MLIR—where he tackled performance bottlenecks, improved gradient utilities, and handled hardware intrinsics and control-flow simplifications. Ludger combines low-level compiler optimization skills with practical ML insight to make gradient computation faster and more reliable for real-world workloads. Colleagues rely on him for performance-driven engineering that bridges research-grade techniques and production constraints.
8 years of coding experience
Github Skills (13)
compiler10
automatic-differentiation10
compiler-compiler10
c-language10
cprogramming-language10
gradient10
optimization10
llvm10
enzyme9
cpp8
machine-learning7
deep-learning6
deeplearning-ai6
Programming languages (12)
Github contributions (5)
EnzymeAD/Enzyme
May 2021 - Jan 2023
High-performance automatic differentiation of LLVM and MLIR.
Role in this project:
Back-end Developer & Optimization Engineer
Back-end Developer & Optimization EngineerContributions:29 reviews, 67 commits, 29 PRs in 1 year 7 months
Contributions summary:Ludger focused on optimizing the Enzyme compiler for automatic differentiation, specifically targeting LLVM and MLIR. They improved gradient calculation utilities and addressed performance bottlenecks by implementing LULESH-motivated optimizations. Furthermore, they handled specific hardware intrinsics (nvvm sqrt) and simplified control flow within the Enzyme codebase. They also made updates to testing portions of the code.
clangautomatic-differentiationsimulationcompilertensorflow
EnzymeAD/Enzyme-Tutorial
Nov 2021 - May 2022
Contributions:1 release, 29 commits, 1 PR in 6 months
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