Ferdinand Mom
Research Engineer at Hugging Face
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolFerdinand Mom is a research engineer based in Paris with 7 years of experience building and optimizing machine and deep learning systems, with a strong focus on high-performance computing and model compression. He has shipped production-grade research at companies like Hugging Face and Criteo—implementing attention kernels, post-training quantization, and RWKV-related work accepted to EMNLP—and contributes to open-source ML tooling such as the well-known River online-ML project by adding robust SQL streaming and SQLAlchemy support. Comfortable across Python, PyTorch, CUDA, C++ and low-level SIMD/assembly, he bridges algorithmic research and performant system implementation for GPU clusters and edge inference. Currently the sole member of Hugging Face’s distributed Transformers effort, he also teaches distributed GPU topics at EPITA and pursues advanced studies at ENS Paris-Saclay, a mix that keeps him grounded in both theory and hands-on scaling challenges.
7 years of coding experience
1 year of employment as a software developer
Master Mathématiques Vision Apprentissage (MVA), Master Mathématiques Vision Apprentissage (MVA) at ENS Paris-SaclayExchange Semester Computer Engineering, Exchange Semester Computer Engineering at Inha University 인하대학교Master of Science - Computer Science & Engineering, Master of Science - Computer Science & Engineering at EPITA: Ecole d'Ingénieurs en Informatique
French, English, Khmer, Spanish
Stackoverflow
59reputation
1kreached
0answers
9questions
Github Skills (20)
database-api10
online-machine-learning10
python10
databases10
sqlalchemy10
sql10
streaming-data10
database10
testing9
data-engineering9
incremental-learning9
machine-learning8
data-science7
computer-vision6
cuda6
Programming languages (9)
Github contributions (5)
online-ml/river
Mar 2020 - Apr 2020
🌊 Online machine learning in Python
Role in this project:
Data Engineer
Data EngineerContributions:10 commits, 1 PR, 11 comments in 7 days
Contributions summary:Ferdinand primarily contributed to the project by implementing and testing database interaction functionalities within the `river` online machine learning framework. They introduced an `iter_sql` function to stream data from SQL databases, including support for different database connection methods. The contributions included creating SQL database setup scripts, writing tests, and integrating SQLAlchemy, demonstrating expertise in data access and data processing within the context of online machine learning.
stream-processingpythonstreaming-datadata-scienceonline-statistics
LABouteille/torchcompress
Sep 2021 - Jul 2022
Contributions:78 commits, 7 pushes, 1 branch in 10 months
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