Guillaume Lample
Co-founder & Chief Scientist at Mistral AI
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolGuillaume Lample is a research-driven AI founder and scientist with 11 years of experience building foundational multilingual and machine‑learning systems, currently co‑founding and serving as Chief Scientist at Mistral AI in Paris. Previously a Research Scientist and PhD candidate at Facebook AI Research, he contributed to high‑impact open‑source projects such as XLM, MUSE and UnsupervisedMT, improving data pipelines, training scalability and multilingual preprocessing for widely used cross‑lingual models. His work spans unsupervised machine translation, symbolic mathematics and automated theorem proving, combining rigorous academic training from École Polytechnique and CMU with practical production engineering. Notably, he has contributed low‑level fixes and training optimizations (gradient clipping, split data loading, multi‑node handling) that helped make research codebases more robust for real‑world experiments.
11 years of coding experience
3 years of employment as a software developer
Master's degree Mathématiques et informatique, Master's degree Mathématiques et informatique at École PolytechniqueMaster's degree Intelligence artificielle, Master's degree Intelligence artificielle at Carnegie Mellon UniversityDoctor of Philosophy - PhD Artificial Intelligence, Doctor of Philosophy - PhD Artificial Intelligence at Pierre and Marie Curie University
Github Skills (15)
data-preprocessing10
machine-translation10
machine-learning10
pytorch10
bash10
nlp10
python10
natural-language-processing10
data-pipelines9
utf9
data-pipeline9
data-loading9
script8
git8
scripting8
Programming languages (7)
Github contributions (5)
facebookresearch/XLM
Feb 2019 - Aug 2019
PyTorch original implementation of Cross-lingual Language Model Pretraining.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 commits, 12 PRs, 47 pushes in 6 months
Contributions summary:Guillaume primarily contributed to data pipeline fixes, and evaluation scripts. The commits included modifications to data loading scripts related to the XNLI dataset and updates to the GLUE evaluation scripts. Additionally, the user implemented the feature of gradient clipping and support for split training data loading, indicating a focus on optimizing model training and data handling. The user also made modifications to the training process, including causal prediction context support and multi-node job termination.
language-modelpytorch
facebookresearch/MUSE
Jan 2018 - Feb 2019
A library for Multilingual Unsupervised or Supervised word Embeddings
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:32 commits, 6 PRs, 33 pushes in 1 year 2 months
Contributions summary:Guillaume contributed to bug fixes, particularly in the `src/utils.py` file, addressing issues related to recentering. The user implemented UTF-8 encoding across multiple files, enhancing compatibility. They also added an experiment name and removed dead code. Additionally, they made adjustments to the embedding export functionality and made several build and library upgrades.
word-embeddings
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