Maxime Fournes
Chief Executive Officer at Pause IA
France
Summary
🤩
Rockstar🎓
Top SchoolMaxime Fournes is a former deep learning and ML researcher turned CEO who has spent 11 years building production-grade AI systems and leading data science teams across finance and industry. He cofounded and now leads Pause IA in France, steering efforts to promote responsible AI and raise awareness of catastrophic AI risks while drawing on hands-on engineering experience. Previously Maxime ran advanced document-understanding and entity-matching initiatives at Two Sigma and contributed core back-end and TensorFlow integrations to the Seldon open-source MLOps platform, showing a blend of research rigor and production engineering. Trained at Cambridge and Centrale Lyon in advanced mathematics and physics, he pairs theoretical depth with practical systems delivery. Colleagues describe him as a pragmatic scientific leader who moves quickly from novel algorithms to scalable microservices and governance-driven AI strategy.
11 years of coding experience
8 years of employment as a software developer
Part III of the Mathematical Tripos Mathematics, Part III of the Mathematical Tripos Mathematics at University of CambridgeClasse Préparatoire Mathematics and Physics, Classe Préparatoire Mathematics and Physics at Lycée Sainte-GenevièveGrande Ecole Mathematics physics and economics, Grande Ecole Mathematics physics and economics at Centrale Lyon
French, English
Github Skills (22)
kubernetes10
docker10
deploying10
python10
back-end-development10
protocol-buffers10
machine-learning10
dockers10
java10
javas10
tensorflow10
kubernetes-pods10
protobuf10
apidoc9
mlops9
Programming languages (10)
Github contributions (5)
SeldonIO/seldon-core
Feb 2017 - Mar 2018
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:238 commits, 19 PRs, 16 pushes in 1 year
Contributions summary:Maxime primarily contributed to the back-end of the system by modifying the data types of identifiers in the protocol buffer definitions, indicating a focus on the core data model and potentially database interactions. They also copied and integrated significant portions of code from the `seldon-engine` repository, suggesting involvement in the core engine functionality and possibly API implementation. These contributions include bug fixes related to data types and code updates to support the general infrastructure of the project.
monitorservingdata-sciencedeploymentmachine-learning
SeldonIO/seldon-server
Aug 2016 - Sep 2016
Machine Learning Platform and Recommendation Engine built on Kubernetes
Role in this project:ML Engineer
ML EngineerContributions:21 commits in 20 days
Contributions summary:Maxime primarily focused on integrating and extending TensorFlow support within the Seldon platform. Their work included developing a `TensorFlowWrapper` class to enable the use of TensorFlow models, adding this wrapper to the dependencies and integrating it into the system. The user also contributed an example demonstrating the use of TensorFlow for MNIST digit classification. This example was implemented by creating a microservice to serve the model.
gcppythontensorflowawsprediction
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