Arthur Zucker
Head Of Transformers at Hugging Face
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolArthur Zucker is Head of Transformers at Hugging Face with seven years of hands-on experience building and maintaining flagship open-source ML libraries like transformers and tokenizers. Based in Paris, he blends deep NLP and speech-processing expertise—evidenced by optimizations to Wav2Vec2 phoneme tokenization—with low-level systems work on fast, production-ready tokenizers and language bindings. He progressed from research internships in bioacoustics and computer vision to core maintainer and engineering leadership roles, combining rigorous academic training (ENS Paris-Saclay, Sorbonne) with practical deployment skills. Notably, he contributes to performance- and integration-focused internals of widely used repos at Hugging Face, making him equally comfortable refactoring C++/Rust backends and improving Python/Node bindings.
7 years of coding experience
3 years of employment as a software developer
Master's degree Mathematics and Computer Science, Master's degree Mathematics and Computer Science at ENS Paris-SaclayMathematics and Computer Science, Mathematics and Computer Science at Polytech SorbonneEngineer's degree Mathematics and Computer Science, Engineer's degree Mathematics and Computer Science at Sorbonne Université
French, English, Japanese, Spanish
Github Skills (10)
speech-recognition10
nlp10
rust10
python10
natural-language-processing10
transformers9
pytorch9
bert7
language-model7
gpt7
Programming languages (9)
Github contributions (5)
huggingface/tokenizers
Apr 2022 - Jul 2026
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Role in this project:
Back-end Developer
Back-end DeveloperContributions:14 releases, 218 reviews, 158 PRs in 4 years 3 months
Contributions summary:Arthur primarily contributed to the core functionalities of the `tokenizers` library, as evidenced by their work in the `tokenizers/src` directory. Their contributions involved refactoring and implementing features like byte fallback for unigram models and enhancements to the added vocabulary functionalities. Additionally, the user addressed build and test issues, including Python and Node.js bindings, showcasing expertise in the library's internal workings and its various integration points.
nlptransformer-architecturelanguage-modelnatural-language-understandingtransformers
huggingface/transformers
Apr 2022 - Jan 2023
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:40 releases, 6814 reviews, 57 commits in 9 months
Contributions summary:Arthur's commits primarily focus on the optimization of the Wav2Vec2 phoneme CTC tokenizer, suggesting a focus on audio-related machine learning tasks. Their contributions include solving heading rendering issues, optimizing the phoneme tokenizer using the phonemizer library, and suggesting documentation improvements. These actions point to expertise in NLP, particularly in the domain of speech recognition or speech processing, aligning with the repository's topics.
audioinferencemachine-learning-modelsmultimodaltransformers
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