Pablo Montalvo
Machine Learning Engineer at Hugging Face
Greater Paris Metropolitan Region France
Summary
🤩
Rockstar🎓
Top SchoolPablo Montalvo is a machine learning engineer at Hugging Face with six years of experience bridging cutting-edge research and production ML. He holds a PhD in physics and transitioned from research roles at Rakuten—where he led data science efforts and published on defenses against adversarial examples—to contributing core model integrations in the widely used Hugging Face Transformers library. At Hugging Face he has driven the implementation, testing, documentation, and robustness fixes for the Fuyu model, demonstrating care for both model correctness and developer experience. Based in Greater Paris, he combines strong mathematical modelling from a coastal-wave research background with practical ML engineering, making him adept at turning theoretical ideas into reliable, open-source tooling. An avid learner, he brings a researcher’s rigor to production-grade contributions in one of the most influential ML ecosystems.
6 years of coding experience
7 years of employment as a software developer
Master's degree, Physics, Ranked 2nd., Master's degree, Physics, Ranked 2nd. at University of Montpellier
French, English, Spanish, Chinese
Github Skills (12)
transformers10
modeler10
model-building10
pytorch10
machine-learning10
nlp10
python10
natural-language-processing10
modeling10
model-driven10
testing10
image-processing9
Programming languages (3)
Github contributions (5)
huggingface/transformers
Sep 2023 - Jul 2026
🤗 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
ML EngineerContributions:559 reviews, 95 PRs, 326 pushes in 2 years 10 months
Contributions summary:Pablo's commits primarily focus on adding and refining the Fuyu model to the Hugging Face Transformers library. This includes the initial implementation, adding a processor, and addressing various issues to ensure the model's functionality. The contributions involve writing tests, adding documentation, and fixing processing-related bugs, demonstrating a strong focus on integrating the model into the existing library infrastructure and ensuring its usability and correctness.
audioinferencemachine-learning-modelsmultimodaltransformers
molbap/transformers
Sep 2023 - Aug 2025
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Contributions:301 pushes, 23 branches in 1 year 11 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.