Sébastien Poirier
Principal Software Engineer at H2O.ai
Prague, Prague, Czechia
Summary
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RockstarSébastien Poirier is a Principal Software Engineer based in Prague with eight years of experience building scalable machine learning systems. At H2O.ai since 2018, he specializes in productionizing AutoML features, notably enhancing target encoding for multinomial targets, missing-value handling, and robust blending frames. He combines deep ML engineering with strong software craftsmanship—adding thorough tests and pragmatic bug fixes to improve model reliability in distributed environments. An active contributor to the well-known open-source H2O-3 platform, he brings practical expertise turning research-grade algorithms into production-ready components. He pairs technical depth with a focus on data quality and reproducibility, often tackling subtle edge cases that quietly improve model performance.
7 years of coding experience
English, German, French, Czech
Github Skills (8)
machine-learning10
automl10
data-science10
python9
data-pipeline7
ensembles7
data-pipelines7
cross-validation7
Programming languages (6)
Github contributions (5)
h2oai/h2o-3
Jun 2018 - Dec 2022
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:802 reviews, 773 commits, 449 PRs in 4 years 6 months
Contributions summary:Sébastien primarily contributed to the development and maintenance of the H2O-3 project, focusing on enhancements and bug fixes for the target encoding feature in the AutoML framework. Their work included improving the handling of missing values and the creation of encoding maps for multinomial target variables. They also implemented a new blending frame for the target encoding and improved the overall quality of the target encoding integration, as well as added testing in various areas for more robust features.
xgboostgampythonk-meansautoencoders
openml/automlbenchmark
Oct 2018 - Jul 2022
OpenML AutoML Benchmarking Framework
Contributions:16 releases, 324 reviews, 648 commits in 3 years 9 months
benchmarkingopenmlmachine-learningbenchmarkautoml
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