Jan Matas is a VP of Engineering and founder with 11 years of experience building developer-focused platforms and high-performance systems, currently leading the engineering effort at Kontext to create the largest ad platform for AI chatbots. Previously CTO at Deepnote, he combines product-minded technical leadership with hands-on systems work—from notebook and data-science infrastructure to low-level simulation code. His background includes SRE and scalable systems at Google, production analytics at Palantir, and low-latency model-serving improvements at Two Sigma, showing fluency across Java, TypeScript, and Python ecosystems. An Imperial College MEng in AI graduate who topped his year, he also contributes to open-source physics tooling, having enhanced Bullet Physics’ soft-body rendering and memory management—an uncommon blend of ML infrastructure and real-time simulation expertise.
11 years of coding experience
5 years of employment as a software developer
Master of Engineering (MEng), Computing - Artificial Intelligence, Top of the year student, Master of Engineering (MEng), Computing - Artificial Intelligence, Top of the year student at Imperial College London
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
Role in this project:
Back-end Developer
Contributions:5 commits, 3 PRs, 7 comments in 2 months
Contributions summary:Jan primarily focused on enhancing the Bullet Physics SDK's support for soft bodies, particularly within the OpenGL rendering pipeline. Their contributions included implementing the necessary rendering infrastructure within the OpenGLGuiHelper, which involved calculating and rendering vertices for soft bodies. The user also modified the PhysicsServerCommandProcessor to facilitate loading and managing soft body objects. Furthermore, the user made adjustments to memory management within the simulation.
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