Sander Land is an AI researcher and seasoned ML engineer with 7 years of industry experience and a DPhil in Computer Science from the University of Oxford, currently applying his expertise at Writer in Oslo. He has led ML teams and built production-grade, scalable systems across startups and enterprises—from reward modelling and evaluation frameworks at Cohere to multi-objective transformer models and retrainable deployments at Chatdesk. His work spans the full stack and research spectrum, combining low-level C++ optimizations (contributions to KataGo) with Python-based tooling and UI improvements (Kivy, KaTrain). Notably, he blends rigorous academic research in computational cardiac modelling with practical machine learning productization, and has driven tokenization research that earned paper awards. Pragmatic and detail-oriented, he focuses on measurable real-world impact through robust engineering and reproducible evaluation.
6 years of coding experience
12 years of employment as a software developer
D.Phil. Computer Science, D.Phil. Computer Science at University of Oxford
BSc BSc MSc Computer science Mathematics, BSc BSc MSc Computer science Mathematics at University of Groningen
Improve your Baduk skills by training with KataGo!
Role in this project:
Full-stack Developer
Contributions:32 releases, 53 reviews, 181 commits in 3 years 2 months
Contributions summary:Sander's commits focus on enhancements to the KaTrain project. They are primarily concerned with improving user interface (UI) elements, fixing bugs, and adding new features, indicating front-end contributions. Backend contributions are also evident, as settings, engine, and game logic are being modified. The commits show improvements to the user interface, core game logic, and engine.
Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS
Role in this project:
Full-stack Developer
Contributions:14 reviews, 8 commits, 10 PRs in 1 year 7 months
Contributions summary:Sander primarily contributed to bug fixes, documentation improvements, and code optimizations within the Kivy framework. Their work included addressing issues in logging, enhancing documentation for KV language and `on_dropfile` functionality, and improving resource handling with caching. They also addressed issues related to EventDispatcher error messages and fixed sound playback issues in ffpyplayer. Their contributions span across various areas of the framework, demonstrating a focus on quality and usability.
pythonlinux-windowswindowsui-frameworklinux
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