Guillaume Genthial

Paris, Ile-de-France

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Summary

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Rockstar

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Top School

Guillaume Genthial is an NLP-focused machine learning engineer with ten years of experience, based in Paris. He has led AI work from research to production—building clinical information extraction systems at Roam Analytics, serving as Senior ML Engineer at Criteo AI Lab and as AI Lead at MetaMap—while bridging modeling and deployment. His open-source contributions include a TensorFlow LSTM-CRF sequence tagging repo, an im2latex seq2seq+attention implementation (data generator and decoder work), and enhancements to Stanford's widely used CS230 code examples such as hyperparameter search and logging. Trained at École Polytechnique and Stanford, he combines rigorous academic grounding with practical skills in Python, TensorFlow/PyTorch and build automation. He routinely operates at the intersection of model engineering and DevOps, enabling robust NLP pipelines for sensitive domains like clinical text.

10 years of coding experience

4 years of employment as a software developer

Master of Science (M.Sc.), Master of Science (M.Sc.) at Stanford University

Master of Science (M.Sc.), Master of Science (M.Sc.) at Ecole polytechnique

Licence 3, Licence 3 at Université Paris 1 Panthéon-Sorbonne

CPGE, CPGE at Lycée Sainte Geneviève

French, English, German, Spanish

Github Skills (19)

named-entity-extraction10

pytorch10

named-entity-recognition10

entity-extraction10

python10

machine-learning10

tensorflow210

lstm10

tensorflow10

data-processing10

hyperparameter-tuning10

build-automation9

crf9

nlp9

seq2seq8

Programming languages (4)

TypeScriptC++CSSPython

Github contributions (5)

guillaumegenthial/sequence_tagging

Mar 2017 - Sep 2018

Named Entity Recognition (LSTM + CRF) - Tensorflow

Role in this project:

ML Engineer

Contributions:26 commits, 6 PRs, 23 pushes in 1 year 6 months

Contributions summary:Guillaume implemented core functionalities for a Named Entity Recognition (NER) model using an LSTM-CRF architecture in TensorFlow. Their contributions included adding character-level LSTM embeddings to the model to enhance performance and improving the overall structure by incorporating docstrings. The user also introduced various features, showcasing the refinement and expansion of the NER model.

nlpnamed-entity-recognitionentity-recognitionentitylstm

cs230-stanford/cs230-code-examples

Jan 2018 - Feb 2018

Code examples in pyTorch and Tensorflow for CS230

Role in this project:

ML Engineer

Contributions:40 commits, 2 PRs, 36 pushes in 21 days

Contributions summary:Guillaume implemented and refined components related to machine learning model training and hyperparameter tuning within a PyTorch and TensorFlow code example repository. They added logging and JSON dump functionality to the utility files, enhancing the model's operational capabilities. Further contributions included the addition of a hyperparameter search feature, allowing for systematic experimentation with different learning rates. The user also made subsequent code cleanup and naming improvements.

pytorchdeep-learningcomputer-visiontensorflownatural-language-processing

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