Michal Kurka
Senior Software Engineer at H2O.ai
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMichal Kurka is a Senior Software Engineer with nine years of professional experience, currently building scalable machine learning and backend systems at H2O.ai from New York. He brings a strong combined background in software engineering and data science, contributing to the H2O-3 open-source ML platform with work that improves model components and robustness under concurrency. Previously he led engineering efforts at Vendavo and cut his teeth in senior development roles across European and US firms, pairing hands-on coding with mentorship and delivery. Holding a Master's in Computer Science from Charles University, Michal favors pragmatic, test-driven improvements that surface in quieter but high-impact contributions—like hardening isotonic regression tests and edge-case URL handling in a widely used ML library.
9 years of coding experience
8 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Charles University
English, Czech
Github Skills (8)
machine-learning10
h2o10
python10
testing10
regression9
collaboration7
open-source7
collaborative-editing7
Programming languages (5)
Github contributions (5)
h2oai/h2o-3
Jul 2016 - Nov 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 & Backend Engineer
Data Scientist & Backend EngineerContributions:1076 reviews, 4474 commits, 2632 PRs in 6 years 4 months
Contributions summary:Michal appears to be involved in the development and maintenance of the H2O-3 platform, focusing on machine learning aspects within a data science context. Their contributions include modifying and testing the Isotonic Regression component of the platform, and also adding testing for a scenario with a long URL. The commits demonstrate the user's interest in model performance. The commits also demonstrate a focus on ensuring that the library functions correctly when running in a concurrent environment and improving the robustness of test cases.
xgboostgampythonk-meansautoencoders
h2oai/mojoland
Aug 2018 - Sep 2022
Testing ground to ensure mojos backward compatibility
Contributions:5 reviews, 6 PRs, 12 pushes in 4 years 1 month
testingtesting-groundbackward-compatibilitycompatibilityensure
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