Filip Matzner
Chief Technology Officer at Qminers
Prague, Czechia
Summary
🤩
Rockstar🎓
Top SchoolFilip Matzner is a Chief Technology Officer and former AI startup co-founder with 11 years of experience building high-performance systems for algorithmic trading and production ML. He holds a PhD in Theoretical Computer Science and Artificial Intelligence, where he researched random recurrent neural networks for time-series forecasting, and combines that research rigor with hands-on C++ and systems engineering. At Qminers he drives technology strategy across the full stack, and previously led product development, DevOps and ML at Iterait, IBM and Cognexa. An active open-source contributor, he has improved core C++ range algorithms and performance-critical GPU/CPU kernels and made substantial build-system and CUDA/MKL integrations for TensorFlow C++ packaging. He is skilled in Unix, algorithms and low-level optimization, frequently moving ideas from academic proofs to production-grade code. Based in Prague, he blends academic depth with pragmatic engineering leadership across startups and enterprise projects.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Theoretical Computer Science and Artificial Intelligence, Doctor of Philosophy (PhD), Theoretical Computer Science and Artificial Intelligence at Charles University in Prague
English, Czech, German
Github Skills (25)
algorithm10
algorithms10
c-language10
scripting10
ranges10
cmake10
data-structure10
script10
tensorflow10
gpu10
performance-optimization10
bazel10
build-automation10
arrayfire10
cuda10
Programming languages (10)
Github contributions (5)
FloopCZ/tensorflow_cc
May 2017 - Jun 2022
Build and install TensorFlow C++ API library.
Role in this project:
DevOps Engineer & Build Engineer
DevOps Engineer & Build EngineerContributions:16 releases, 320 commits, 153 PRs in 5 years 1 month
Contributions summary:Filip primarily focused on improving the build and deployment process for the TensorFlow C++ API library. They made significant changes to the build scripts (build_tensorflow.sh, copy_links.sh), updated the Bazel configuration, and incorporated changes related to CUDA support and Intel MKL integration. Additionally, the user updated the TensorFlow version and made various improvements to support static and shared library builds, showing a strong understanding of build automation and dependency management.
apicppc-plus-plusapi-librarytensorflow
arrayfire/arrayfire
Jun 2016 - Apr 2018
ArrayFire: a general purpose GPU library.
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:4 reviews, 6 commits, 5 PRs in 1 year 10 months
Contributions summary:Filip primarily focused on improving the performance of CPU-based copy kernels within the ArrayFire library. They refactored the copy functionality, optimizing memory operations using `memcpy` and by simplifying the code structure using recursion. The user also updated the build configuration to address potential build issues and ensured CUDA compatibility by updating the toolkit driver versions. Their contributions demonstrate a strong understanding of low-level optimization techniques and GPU-accelerated computing.
cudaarrayfirecppgpuscientific-computing
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