Julien Denize
Machine Learning Engineer at Mistral AI
Palaiseau, Île-de-France, France
Summary
🤩
Rockstar🎓
Top SchoolJulien Denize is a machine learning engineer with a PhD in deep learning and 7 years of experience bridging research and production-grade AI systems. He has built MLOps stacks that scaled an 80+ researcher R&D team and supported a generative AI proof-of-concept serving 20,000 users, demonstrating a rare blend of infra, tooling and model expertise. At Mistral AI he maintains open-source libraries and contributes to widely-used projects like vLLM and Transformers, focusing on multimodal generation and agent capabilities. His background includes state-of-the-art self-supervised research, efficient PyTorch libraries for pretraining and on-GPU augmentation, and hands-on cluster optimization with Slurm and Kubernetes. While deepening backend skills, he’s actively expanding into frontend development to deliver end-to-end AI applications. Based in Palaiseau, France, he pairs academic rigor with practical engineering to make research reproducible and deployable.
7 years of coding experience
5 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Institut national des Sciences appliquées de RouenTheoretical and Mathematical Physics, Joined Télécom SudParis via the Concours Mines-Télécom, Theoretical and Mathematical Physics, Joined Télécom SudParis via the Concours Mines-Télécom at CPGE Lycée ChateaubriandMaster of Engineering, Major in Artificial Intelligence and minor in Software Engineering, Master of Engineering, Major in Artificial Intelligence and minor in Software Engineering at Télécom SudParis
French, English
Stackoverflow
798reputation
17kreached
34answers
8questions
Github Skills (62)
lightning10
pytorch10
artificial-intelligence10
robotics10
python10
evaluation10
image-processing10
lifecycle10
llama10
machine-learning10
pandas10
inference10
tpu10
data-annotation10
amd10
Programming languages (3)
Github contributions (5)
juliendenize/torchaug
Jun 2023 - Feb 2025
Library to perform efficient vision data augmentations for CPU/GPU per-sample/batched data.
Contributions:24 releases, 3 reviews, 87 PRs in 1 year 8 months
data-augmentationdeep-learninggpuimage-processingpytorch
CEA-LIST/SCE
Sep 2022 - Jan 2023
Contributions:2 commits, 8 pushes in 3 months
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