Summary
Jack Douglas is a founding engineer building decentralized web infrastructure at Tonk Labs, with eight years of experience bridging research-grade ML and production software. Previously a RIMSE Research Fellow at UCSD's ATLAS group, he developed GNN-based tools to predict dynamic mechanical loss and adapted PANNA to train high-dimensional neural potentials for MOFs. Trained in EECS at UC Berkeley, Jack combines deep technical rigor with product-minded engineering—moving models from research prototypes toward real-world systems. Based in London, he focuses on "building the new internet" and exploring alternate futures, signaling a strong interest in decentralized and emergent architectures. Notably, his background spans both materials-physics ML and systems engineering, giving him an uncommon cross-disciplinary perspective useful for infrastructure that must integrate models, data and hardware.
9 years of coding experience
Bachelor of Engineering - BE, Electrical Engineering and Computer Science, Bachelor of Engineering - BE, Electrical Engineering and Computer Science at University of California, Berkeley
High School, Computer Engineering, High School, Computer Engineering at Francis Parker School
English, French