Arthur Brussee
Games System Engineer at DeepMind
Amsterdam, North Holland, Netherlands
Summary
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Rockstar🎓
Top SchoolArthur Brussee is a games systems engineer and graphics programmer with nine years of experience building visually rich, performance-critical interactive systems, currently working at DeepMind in Amsterdam. With a foundation in physics and advanced study in computer graphics and vision, he has led graphics for acclaimed titles like Ori and the Blind Forest and Manifold Garden, and contributed tooling and optimization across VR and AR projects. He is a seasoned Unity developer and shader/tooling author who also founded Tiny Cube Studio, delivering high-end GPU particle middleware and visualization tools. Beyond game engines, Arthur contributes low-level performance work to open-source ML infrastructure—helping optimize the Rust-based Burn framework’s WGPU backend and allocation paths—reflecting his interest in combining graphics with machine learning and XR. Colleagues rely on him for hard technical problems, cross-disciplinary thinking, and pragmatic optimizations that make complex visuals run smoothly at scale.
9 years of coding experience
8 years of employment as a software developer
BSc, Physics, BSc, Physics at Universiteit UtrechtUniversity College London
English, Dutch
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
deeplearning-ai10
rust10
deep-learning10
concurrency10
high-performance9
tensor9
autodiff9
machine-learning7
Programming languages (8)
Github contributions (5)
tracel-ai/burn
Mar 2024 - Mar 2025
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 reviews, 32 PRs, 35 comments in 1 year
Contributions summary:Arthur primarily contributed to the `burn` deep learning framework, focusing on low-level implementations and performance improvements. Their work involved adding features for initializing the Wgpu backend with existing devices, which included making necessary adjustments to data structures and synchronization mechanisms. They also optimized the client.create function for small allocations, enhancing overall performance. Additional contributions involved making core parameters public, and adjustments to the sync/async handling.
rustyburnndarraydeep-learningrust
ArthurBrussee/brush
Oct 2024 - Apr 2025
3D Reconstruction for all
Contributions:2 reviews, 111 PRs, 437 pushes in 5 months
gaussian-splattinggraphicsreconstruction
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