Jan Gosmann
Senior Consultant at TNG Technology Consulting
Bavaria, Germany
Summary
🤩
RockstarJan Gosmann is a Senior Consultant at TNG Technology Consulting with 16 years of software engineering experience and a PhD in computational neuroscience, blending deep research expertise with practical engineering. He specializes in AI, machine learning, and cognitive systems, and has a strong track record optimizing complex simulation software—most notably accelerating the core Nengo simulator up to sevenfold and maintaining Nengo SPA for spiking cognitive models. Proficient in Python and backend optimization, he also improves developer workflows through caching refactors, testing, and logging enhancements. An active open-source contributor, he has applied his systems thinking to projects ranging from large-scale brain-model tooling to keyboard layout rules for Karabiner-Elements. Based in Bavaria, he pairs analytical rigor with hands-on problem solving, and outside work enjoys rock climbing and baking bread—a hint at his blend of precision and creativity.
16 years of coding experience
German, English, Japanese
Stackoverflow
750reputation
71kreached
13answers
3questions
Github Skills (26)
json10
caching10
python10
testing10
karabiner10
kbar10
numpy10
keyboard-layout10
model-optimization9
macos9
configuration-management8
artificial-neural-networks7
algorithm7
algorithms7
neural-network7
Programming languages (24)
Github contributions (5)
nengo/nengo
Apr 2014 - Jan 2019
A Python library for creating and simulating large-scale brain models
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:2 reviews, 253 commits, 206 PRs in 4 years 9 months
Contributions summary:Jan primarily focused on refactoring the caching mechanism for decoder solutions. They implemented a new class for basic decoder caching, optimized the caching process by implementing a Nengo cache object, and added functionality to limit and invalidate items in the cache. Further contributions included adding logging messages, warnings, and tests for the caching classes to improve the performance and robustness of the model building process. The user demonstrated expertise in Python programming, model optimization, and testing.
pythonnengoneuroscienceneural-networks
pqrs-org/KE-complex_modifications
Oct 2017 - Apr 2022
Karabiner-Elements complex_modifications rules
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:8 reviews, 28 commits, 12 PRs in 4 years 7 months
Contributions summary:Jan primarily contributed to the implementation of the Neo2 keyboard layout within the Karabiner-Elements complex modifications rules. Their work included defining layer 4 and 6 mappings, enabling caps lock functionality, and addressing issues with shortcuts. They added the Neo2 layout to the documentation and incorporated Windows keyboard specific rules, as well as, fixing bugs.
karabiner
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.