Florent Gbelidji
Member Of Technical Staff - Open Source Strategy Lead
Paris, Ile-de-France, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolFlorent Gbelidji is an applied AI and open-source strategy lead with five years of hands-on experience building ML products and developer ecosystems from Paris. He has progressed through technical and partnership leadership roles at Hugging Face and now leads open-source strategy at Reflection AI, combining deep engineering with ecosystem-building. His contributions to notable open-source projects include enabling multilabel few-shot learning in Hugging Face’s popular SetFit repo, reflecting a focus on practical, production-ready ML tooling. Trained in electrical engineering and computational mathematics at CentraleSupélec and Université Paris Cité, he bridges rigorous research with applied deployment, especially around transformer-based models. Colleagues know him for translating complex ML research into usable pipelines and for mentoring engineers and students in production-focused transformer applications.
5 years of coding experience
5 years of employment as a software developer
PCSI, PC, Mathematics, Physics, Chemistry, PCSI, PC, Mathematics, Physics, Chemistry at Collège StanislasMaster of Science - MS, Electrical Engineering and Computer Science, Master of Science - MS, Electrical Engineering and Computer Science at CentraleSupélecBaccalauréat, Baccalauréat at Lycée Militaire d’AutunMaster's degree (MSc), Computational and Applied Mathematics, Master's degree (MSc), Computational and Applied Mathematics at Université Paris Cité
English, French, Spanish, Russian
Stackoverflow
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Github Skills (6)
scikit-learn10
machine-learning10
nlp10
sentence-transformers10
python10
scikit10
Programming languages (4)
Github contributions (5)
huggingface/setfit
Aug 2022 - Sep 2022
Efficient few-shot learning with Sentence Transformers
Role in this project:
ML Engineer
ML EngineerContributions:17 commits in 1 month
Contributions summary:Florent primarily contributed to the implementation of multilabel classification support within the SetFit framework. This involved developing new functions for generating sentence pairs for multilabel cases, creating few-shot splits specifically tailored for multilabel datasets, and loading these dataset splits. They also added and modified code for the SetFit training pipeline to accommodate multilabel classification.
nlppytorchsentencetransformerslanguage-model
fgbelidji/deep-learning-containers
Feb 2025 - Mar 2025
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Contributions:21 pushes, 2 branches in 28 days
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