Michael Demoret
Engineering Manager at NVIDIA
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Demoret is an engineering manager at NVIDIA in New York with nine years of professional software and systems experience layered on a longer engineering background dating to system engineering roles since 2009. He combines hands-on ML and DevOps skills—contributing to high-profile open-source projects like RAPIDS cuML (enhancing DBSCAN internals) and the Morpheus SDK (streamlining Docker/conda CI/CD)—with practical solutions-architecture experience from a multi-year tenure at NVIDIA. Trained in Engineering Physics at the University of Colorado Boulder, he brings a physics-informed approach to performance-sensitive problems and build automation. Colleagues rely on him to bridge low-level C++/Cython algorithm work and production deployment pipelines, and he has a proven knack for turning research-grade code into robust, automatable releases.
9 years of coding experience
15 years of employment as a software developer
Bachelor of Science (BS) Engineering Physics/Applied Physics, Bachelor of Science (BS) Engineering Physics/Applied Physics at University of Colorado Boulder
Github Skills (19)
docker10
c-language10
python10
machine-learning10
conda10
bash10
machine-learning-algorithms10
dockers10
cicd10
gpu10
build-automation10
cython10
dbscan10
cprogramming-language10
scripting9
Programming languages (8)
Github contributions (5)
nv-morpheus/Morpheus
Apr 2022 - Jan 2023
Morpheus SDK
Role in this project:
DevOps Engineer & Build Automation
DevOps Engineer & Build AutomationContributions:16 releases, 1152 reviews, 101 commits in 9 months
Contributions summary:Michael's commits primarily focused on enhancing the build and deployment processes of the Morpheus SDK. They addressed critical issues in the Docker build process by ensuring the existence of cache directories and optimizing the build process for conda packages. Moreover, they updated the build scripts to incorporate new features, such as specifying conda channel aliases and adding a conda mirror build argument. This indicates a strong focus on automating and streamlining the continuous integration and continuous deployment (CI/CD) pipelines for the Morpheus SDK.
javasdk
rapidsai/cuml
Jun 2020 - Apr 2021
cuML - RAPIDS Machine Learning Library
Role in this project:ML Engineer
ML EngineerContributions:251 reviews, 118 commits, 34 PRs in 9 months
Contributions summary:Michael's commits primarily involve extending the functionality of the `cuml` library, specifically adding support for `core_sample_indices_` to the DBSCAN clustering algorithm. The contributions demonstrate a focus on improving the algorithm's capabilities, including modifications to the C++ and Cython code, as well as updating associated tests to validate the correctness of the added features. These changes directly relate to improving the performance and functionality of the machine learning library.
cudacumlnvidiadata-sciencegpu
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