Nathaniel Simard
Founder & CEO at Tracel Technologies
Quebec, Canada
Summary
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Rockstar🎓
Top SchoolNathaniel Simard is a Founder & CEO and machine learning engineer with a decade of experience building production-grade software and ML systems from research prototypes to startup products. Based in Quebec, he founded Tracel Technologies after roles as an AI developer and research scientist where he adapted Transformer architectures for molecular graphs and shipped graph-based reaction prediction models. He’s a hands-on backend and ML practitioner who contributes to open-source, notably enhancing the Rust-based Burn deep learning framework for efficient, portable model execution and transformer/attention components. Comfortable across Java, C++, Rust and modern ML stacks, he blends software engineering rigor (microservices, data pipelines, test automation) with research-driven model design. Nathaniel’s background shows an uncommon mix of low-level performance focus and applied chemistry-aware ML, enabling practical deployments of advanced models.
10 years of coding experience
5 years of employment as a software developer
Maîtrise ès sciences (M.Sc.) Appentissage automatique, Maîtrise ès sciences (M.Sc.) Appentissage automatique at Mila - Quebec Artificial Intelligence InstituteBaccalauréat en ingénierie (B.ing) Génie logiciel, Baccalauréat en ingénierie (B.ing) Génie logiciel at Université Laval
Stackoverflow
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Github Skills (7)
neural-network10
rust10
deep-learning10
pytorch9
machine-learning9
transformer9
autodiff9
Programming languages (7)
Github contributions (5)
tracel-ai/burn
Jul 2022 - Feb 2023
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:
Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:12 releases, 1172 reviews, 186 commits in 6 months
Contributions summary:Nathaniel's commits primarily focused on refactoring and enhancing the forward pass within the MNIST example. They implemented and refined the neural network model, utilizing various modules for deep learning. The user demonstrates proficiency in configuring and integrating core deep-learning components within the Burn framework, including attention mechanisms and transformer encoders, highlighting their expertise in developing and optimizing models for machine learning.
rustyburnndarraydeep-learningrust
tracel-ai/cubecl
Jul 2024 - Apr 2025
Multi-platform high-performance compute language extension for Rust.
Contributions:2 releases, 314 reviews, 376 PRs in 8 months
cudagpgpugpujitlinalg
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