Jan Hünermann
Member Of Technical Staff at poolside
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJan Hünermann is a machine learning engineer and applied scientist with 11 years of experience building perception and decision systems for autonomous agents, currently at poolside focused on developer-facing AI for software. Previously he led driving-behaviour research at Wayve, co-authoring work like “Driving with LLMs” (ICRA 2024) and optimizing large-scale GPU training to nearly double throughput. His background spans academic and industrial projects in single-image depth estimation, multi-agent reinforcement learning, and robotic perception, with internships at Magazino and BMW Group. Jan is also an active open-source developer—maintaining neurojs, a JavaScript deep learning and RL library where he implemented agents, simulated driving experiments, and stability improvements such as L2 norm clipping. He combines hands-on full-stack prototyping with rigorous research, often translating simulation insights into practical training and deployment gains. Based in London, he brings a pragmatic, experiment-driven approach to turning ML research into production-ready systems.
11 years of coding experience
5 years of employment as a software developer
Exchange Semester, Exchange Semester at National University of SingaporeElectrical Engineering, Electrical Engineering at Technical University of MunichHonours Degree Technology Management, Honours Degree Technology Management at Center for Digital Technology and Management (CDTM)
Latin, German, English
Github Skills (5)
neural-network10
machine-learning10
javascript10
deep-learning10
reinforcement-learning10
Programming languages (6)
Github contributions (5)
janhuenermann/neurojs
Sep 2016 - May 2021
A JavaScript deep learning and reinforcement learning library.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:50 commits, 8 PRs, 43 pushes in 4 years 8 months
Contributions summary:Jan contributed to the development of a JavaScript deep learning and reinforcement learning library. Their work involved implementing reinforcement learning agents and experimenting with car simulations using the library. Key changes include implementing sensors, modifying car behavior, improving the user interface, and refactoring network training components. The user also added L2 norm clipping to improve training stability.
deep-learningjavascriptreinforcement-learninglibrary-learningreinforcement
janhuenermann/social-circles
Dec 2014 - Aug 2021
Contributions:46 commits, 20 pushes, 1 comment in 6 years 8 months
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