Matthias Urban
Senior Software Engineer at GMV
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolMatthias Urban is a senior software engineer based in Munich with 7 years of professional experience and a decade-plus background in media and video systems. He currently drives backend engineering at GMV and previously spent eight years building resilient broadcast and media technology at Institut für Rundfunktechnik. His work spans production-grade system operation, automation, and backend development—early career experience includes operating a large-scale distributed video system across 10+ countries for a space operations project. An active open-source contributor, he has improved Auto-PyTorch by fixing CPU-crash bugs, tightening AutoNet configurations, and enhancing error reporting and runtime/budget checks to make ML automation more robust. He combines deep domain knowledge in media technology (Master’s in Medientechnik) with pragmatic engineering skills that favor reliability and clear diagnostics. Colleagues would note his knack for turning hard-to-debug failures into actionable fixes that improve user experience.
7 years of coding experience
12 years of employment as a software developer
Abitur, Abitur at Justus-von-Liebig Gymnasium NeusäßMaster's degree, Medientechnik, Master's degree, Medientechnik at Deggendorf Institute of Technology
German, English, Spanish
Github Skills (7)
pytorch10
deep-learning10
automl10
python10
error-handling9
tabular8
datatable8
Programming languages (3)
Github contributions (5)
automl/Auto-PyTorch
Dec 2018 - May 2019
Automatic architecture search and hyperparameter optimization for PyTorch
Role in this project:
Back-end Developer
Back-end DeveloperContributions:31 commits, 1 PR, 15 pushes in 4 months
Contributions summary:Matthias primarily addressed issues related to the `autoPyTorch` library. They fixed critical bugs causing crashes on CPUs and corrected misconfigurations of AutoNet. Furthermore, the user improved the error messages for misconfigurations, enhancing user experience. They also updated internal functionalities by introducing new metrics and implementing checks for budget and runtime.
hyperparameter-optimizationpytorchautomldeep-learningtabular-data
simphony/osp-core
Oct 2019 - Mar 2021
Contributions:369 commits in 1 year 5 months
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