Kyle Bayes is a software engineer with seven years of experience building high-performance backend systems at Google and DeepMind, based in Stony Stratford, UK. He blends a strong academic foundation—a PhD and MA in Mathematics plus a 4.0 BS in Mathematics and Computer Science—with practical engineering, shipping improvements to the MuJoCo physics simulator used widely in robotics and ML research. His contributions to collision detection, per-body gravity compensation, and memory management show a talent for optimizing numerically intensive code and improving APIs and documentation. Comfortable navigating research-grade codebases, he focuses on robust engineering that bridges theoretical rigor and production needs.
7 years of coding experience
Master of Arts - MA, Mathematics, Master of Arts - MA, Mathematics at Binghamton University
Bachelor of Science - BS, Mathematics and Computer Science, 4.0, Bachelor of Science - BS, Mathematics and Computer Science, 4.0 at Wright State University
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Role in this project:
Back-end Developer
Contributions:1 release, 1 review, 18 commits in 3 months
Contributions summary:Kyle contributed to the MuJoCo physics simulator by implementing features and addressing bugs related to collision detection and handling. Specifically, they focused on optimizing collision filtering by using squared distance comparisons and added support for per-body gravity compensation. These changes involved modifications to engine files and also encompassed documentation updates related to computation and the API reference. The user also implemented various other changes related to memory management and adding API functions for library loading.
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