Hugo Linsenmaier
Infrastructure Engineer at NVIDIA
San Francisco, California, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolHugo Linsenmaier is an Infrastructure and Machine Learning Infrastructure Engineer with seven years of experience building high-performance, CUDA-accelerated systems from San Francisco with Swiss roots. He has contributed to prominent RAPIDS projects such as raft and cugraph, implementing a brute-force kNN via FAISS integration and optimizing the Force Atlas 2 graph layout for better kernel performance and test reliability. Comfortable across back-end and ML primitives, he focuses on performance tuning, low-level optimization, and robust test harnesses that make research-grade algorithms production-ready. Educated in computer science at EPITA and Tampere University of Technology, Hugo blends formal engineering training with hands-on open-source impact on widely used ML and graph analytics tooling. Notably, his work shows a preference for improving foundational building blocks—kernels, thresholds, and compilation/licensing details—that quietly enable larger system speedups.
7 years of coding experience
Tampere University of TechnologyEngineer's degree, Computer Science, Engineer's degree, Computer Science at EPITA: Ingénierie Informatique
English, French
Github Skills (21)
vector-search10
graph-algorithms10
c-language10
nearest-neighbors10
machine-learning10
graph-analysis10
kernel10
gpu10
cuda10
cprogramming-language10
faiss10
data-structure9
algorithm9
algorithms9
building-blocks9
Programming languages (4)
Github contributions (5)
rapidsai/cugraph
Mar 2020 - May 2021
cuGraph - RAPIDS Graph Analytics Library
Role in this project:
Back-end Developer
Back-end DeveloperContributions:46 reviews, 121 commits, 17 PRs in 1 year 2 months
Contributions summary:Hugo primarily contributed to the `cugraph` library, specifically focusing on performance enhancements and refactoring of the Force Atlas 2 (FA2) layout algorithm. Their work involved updating threshold values, refactoring headers, and optimizing kernel launches within the FA2 implementation. Additionally, the user made modifications to test harnesses for validating layout algorithms, increasing the reliability and performance of the graph library.
cudagraph-analysisanalyticsgraph-analyticsgraphml
rapidsai/raft
Jan 2021 - Jul 2022
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Role in this project:ML Engineer
ML EngineerContributions:3 reviews, 16 commits, 5 PRs in 1 year 6 months
Contributions summary:Hugo primarily contributed to the `raft` repository by implementing and testing a brute force k-Nearest Neighbors (kNN) algorithm. This involved integrating the FAISS library, which is a widely used library for similarity search and clustering. The user also included a test for the implemented kNN algorithm and made updates for compilation and licensing. This demonstrates their focus on contributing to the core functionality of the repository, which involves fundamental algorithms and primitives.
sciencedata-sciencemachine-learninggraph-data-sciencefundamental
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