Jordan Fréry
Head Of Machine Learning at Zama
Greater Lyon Area France
Summary
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Rockstar🎓
Top SchoolJordan Fréry is Head of Machine Learning with nine years of experience building privacy-preserving and large-scale ML systems from research to production. Based in Greater Lyon, he leads ML efforts at Zama after advancing privacy-preserving ML research there—contributing to FHE-focused tooling like Concrete-ML and integrating quantization into PyTorch workflows. His background spans fraud detection at Worldline, production ML at Concirrus, and academic research culminating in a PhD in Mathematics and Computer Science, giving him strong theoretical depth and practical deployment skills. Jordan combines hands-on engineering with research rigor to make AI secure, private, and trustworthy, and he often tackles low-level algorithmic challenges that bridge cryptography and applied ML.
10 years of coding experience
10 years of employment as a software developer
PhD, Mathematics and Computer Science, PhD, Mathematics and Computer Science at Université Jean Monnet Saint-EtienneBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at VIA University College
Github Skills (6)
quantization10
pytorch10
machine-learning10
python10
data-science4
tensorflow4
Programming languages (6)
Github contributions (5)
zama-ai/concrete-ml
Jan 2022 - Jan 2023
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
Role in this project:
Back-end & ML Engineer
Back-end & ML EngineerContributions:2 releases, 725 reviews, 131 commits in 1 year
Contributions summary:Jordan contributed to a privacy-preserving machine learning framework by implementing post-training quantization methods and integrating them with PyTorch. The changes included porting quantization and Torch functionality from a related project, with corresponding code modifications to integrate these features. The commits demonstrate the user's work on specific, low-level changes in quantization-related logic, which would contribute to a new FHE framework.
fhehomomorphic-encryptionmachine-learningpythondata-science
concrete-security/atlas
Nov 2025 - Mar 2026
Contributions:21 reviews, 4 PRs, 14 pushes in 3 months
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