René Hollander
Site Reliability Engineer at Google
Weinfelden, Thurgau, Switzerland
Summary
👤
Senior🎓
Top SchoolRené Hollander is a Site Reliability Engineer at Google Zürich with 11 years of hands-on experience building and optimizing production systems. He combines growth-minded leadership and a doer’s mentality—often rolling up his sleeves to deliver high-impact results across business development, strategic initiatives and engineering. Technically strong in performance engineering, he contributed architecture and AVX-optimized distance calculations to the widely used open-source library Annoy, improving on-disk index builds and core algorithm efficiency. A former Google intern who built debugging tools for planet-scale key-value stores and modernized frontend experiences, René blends deep systems expertise with a talent for spotting and nurturing technical and business opportunities. Based in Switzerland, he values integrity, loyalty and creativity, and has personally invested time and funds into product ventures that reflect his appetite for innovation.
11 years of coding experience
Vienna University of TechnologyHigh School Diploma, Information Technology, High School Diploma, Information Technology at Technologisches Gewerbemuseum
English, Spanish, Hebrew
Github Skills (15)
algorithm10
data-structures10
algorithms10
approximate-nearest-neighbor-search10
c-language10
cpp10
cprogramming-language10
performance-optimization10
cplus10
data-structure10
python7
nearest-neighbors7
nearest-neighbor-search7
go5
lua4
Programming languages (17)
Github contributions (5)
spotify/annoy
Jan 2018 - Nov 2018
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:15 commits, 3 PRs, 22 comments in 10 months
Contributions summary:René primarily contributed to optimizing the performance of the `annoy` library, specifically by hand-vectorizing critical distance calculations and margin calculations using AVX intrinsics. They improved the efficiency of core functions like Euclidean, Angular, and Manhattan distance calculations. The user also refactored the code, fixed pointer math issues, and moved AVX sum calculations into dedicated functions, improving code readability and maintainability. Furthermore, they added support for on-disk index building.
memorymemory-usagepythonapproximate-nearest-neighbor-searchkd-tree
ReneHollander/automute
Dec 2019 - Jun 2023
Contributions:2 releases, 2 pushes, 1 branch in 3 years 6 months
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