Maxime Cuny
Manager Product Security Core Services at Rivian and Volkswagen Group Technologies
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMaxime Cuny is a product security leader with nine years of hands-on experience building secure systems for automotive and enterprise platforms, currently managing Product Security Core Services for Rivian and Volkswagen Group Technologies in San Francisco. He progressed from engineering roles into management after delivering security features and tooling across Rivian and Pure Storage, pairing practical incident knowledge with program-level oversight. A Franco‑American developer with a background in data preparation and ML-focused contributions (notably enhancements to a SimilarityEncoder in the skrub-data project), he blends security engineering with data-savvy problem solving. His career started in research and teaching roles at EPITA and Inria, giving him a strong foundation in computer science and mentorship. Comfortable shipping code and shaping security products, he brings a pragmatic, research-informed approach to securing complex vehicle and cloud ecosystems. Fluent in cross-cultural environments, he leverages both European engineering training and Boston University studies in math and CS to bridge technical depth and product delivery.
9 years of coding experience
6 years of employment as a software developer
Mathematics and Computer Science, Mathematics and Computer Science at Boston University Metropolitan CollegeDiplôme d'ingénieur Ingénierie informatique, Diplôme d'ingénieur Ingénierie informatique at École pour l'Informatique et les Techniques Avancées
English, French
Github Skills (8)
scikit10
machine-learning10
data-cleaning10
python10
data-science10
scikit-learn10
numpy9
data-analysis9
Programming languages (2)
Github contributions (5)
skrub-data/skrub
Oct 2018 - Nov 2018
Prepping tables for machine learning
Role in this project:
Data Scientist
Data ScientistContributions:19 commits, 12 PRs, 5 pushes in 1 month
Contributions summary:Maxime primarily contributed to the `dirty_cat` library within the `skrub-data/skrub` repository, which focuses on preparing tables for machine learning. Their commits modified the `SimilarityEncoder` class, incorporating features related to prototype selection (k-means and most_frequent) for categorical variable encoding and fixed bugs. They also made changes to tests related to the `SimilarityEncoder` and its functionality to ensure accurate results. The user's work demonstrates a focus on data preparation techniques.
autoencoderdata-preprocessingdatadata-sciencetabular-data
mcuny/dirty_cat
Oct 2018 - Dec 2018
Encoding methods for dirty categorical variables
Contributions:4 PRs, 49 pushes, 4 branches in 1 month
categorical-variablesencodingdirtycategoricalvariables
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