Alexander Kuhnle
Software Engineer at Meticulous
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAlexander Kuhnle is a software engineer with a decade of experience specializing in machine learning and bringing AI research into applied products. He holds advanced degrees from Cambridge and KIT and has blended academic supervision with industry roles from founding ML teams to leading AI research and engineering across startups and enterprise (including SS&C Blue Prism and Zebra). An active open-source contributor to Tensorforce, he has made core back-end contributions to reinforcement learning primitives (dueling networks, improved LSTM/GRU, distribution functions) on a project with thousands of stars. Comfortable moving between research and production, he repeatedly ships model architecture and optimization improvements that make cutting-edge ML usable in real systems. Based in London, he combines deep theoretical training with hands-on engineering and early-stage founding experience, making him adept at translating novel AI ideas into deployable software.
10 years of coding experience
8 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 Mathematics Mathematics, BSc Mathematics Mathematics at Karlsruhe Institute of Technology (KIT)
English, German
Github Skills (11)
machine-learning10
tensorflow10
python10
back-end-development10
reinforcement-learning10
data-structure9
computer-engineering9
algorithm9
data-structures9
algorithms9
keras8
Programming languages (6)
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.
reinforcement-learningtensorflowdeep-reinforcement-learningtensorflow-librarytensorforce
AlexKuhnle/alexkuhnle.github.io
Jan 2019 - Jul 2026
Contributions:7 PRs, 26 pushes, 1 branch in 7 years 7 months
reactnextjs
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