Alexander Kuhnle
Founding Engineer at Stealth
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAlexander Kuhnle is a London-based founding engineer and AI leader with a decade of experience turning machine learning research into product-grade systems. He has built and led teams up to 18 people, driving initiatives from LLM optimisation for on-device deployment at Zebra to establishing and productionising Smart Vision and cloud-hosted ML services at Blue Prism. As sole maintainer and lead developer of Tensorforce, he contributed core network layers and architecture improvements to a widely used TensorFlow reinforcement learning library. His background spans hands-on engineering (OCR, GUI understanding, generative tabular models) and research—he holds a PhD from Cambridge and has supervised award-winning MPhil projects. Notably, his career mixes deep RL open-source contributions with practical device-constrained LLM optimisation and past work in bioinformatics, reflecting broad technical curiosity and delivery focus.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of CambridgeBSc Informatics, BSc Informatics at Karlsruhe Institute of Technology (KIT)High School, High School at Gymnasium Rutesheim
English, German
Github Skills (13)
tensorflow210
machine-learning10
tensorflow10
back-end-development10
python10
reinforcement-learning10
datastructures9
datastructure9
computer-engineering9
algorithm9
data-structures9
algorithms9
keras8
Programming languages (5)
Github contributions (5)
tensorforce/tensorforce
Jul 2017 - Jan 2023
Tensorforce: a TensorFlow library for applied reinforcement learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:17 releases, 496 commits, 103 PRs in 5 years 7 months
Contributions summary:Alexander's commits primarily focus on modifications within the Tensorforce library, suggesting a role focused on back-end development. The changes include core network layer implementations such as Dueling and improved LSTM/GRU, along with changes to distribution functions and model optimization processes. The user also contributed to the testing, integration, and architecture improvements of model-related components within the reinforcement learning library.
tensorflow-librarytensorforcereinforcement-learningdeep-reinforcement-learningmachine-learning
AlexKuhnle/alexkuhnle.github.io
Jan 2019 - Oct 2024
Contributions:7 PRs, 25 pushes, 1 branch in 5 years 10 months
reactnextjs
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