William Hicks
Senior System Software Engineer at NVIDIA
Charlotte, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolWilliam Hicks is a Senior System Software Engineer with a decade of experience building high-performance, CUDA-accelerated back-end systems and robust test infrastructure, currently driving system software work at NVIDIA from Charlotte. He blends deep numerical and parallel-computing expertise (MPI/CUDA, SVD solvers, PCA) with a user-centered mindset, ensuring code is optimized for real humans—end users and fellow developers—through careful testing and logging. His open-source contributions to notable RAPIDS projects (cuML, RAFT) and CuPy emphasize test quality, memory-safety, and compatibility across CUDA versions. Earlier roles span research, teaching, and consulting in physics, computer vision, and creative writing, giving him an unusual combination of technical rigor and communication skills. Colleagues rely on him to translate complex algorithms into reliable, production-ready components while improving build and testing workflows.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.Sc.), Literary Arts and Physics, Bachelor of Science (B.Sc.), Literary Arts and Physics at Brown UniversityMaster's Degree, Physics, Master's Degree, Physics at Brandeis University
Stackoverflow
355reputation
9kreached
16answers
0questions
Github Skills (33)
algorithm10
algorithms10
c-language10
pytest10
python10
testing10
pca10
machine-learning10
machine-learning-algorithms10
data-structure10
numpy10
gpu10
cupy10
cuda10
data-structures10
Programming languages (10)
Github contributions (5)
rapidsai/cuml
Aug 2020 - Jan 2023
cuML - RAPIDS Machine Learning Library
Role in this project:
Back-end Developer / ML Engineer
Back-end Developer / ML EngineerContributions:480 reviews, 291 commits, 127 PRs in 2 years 4 months
Contributions summary:William primarily contributed to the cuML library, focusing on Principal Component Analysis (PCA). They implemented tests for default PCA parameters, ensuring compatibility with the sklearn defaults and the library's behavior. They also addressed documentation and code style issues, including fixing broken links and correcting grammatical errors. Furthermore, the user added and enhanced logging capabilities with the addition of a callback, to aid with debugging and performance evaluation.
cudacumlnvidiadata-sciencegpu
rapidsai/raft
Oct 2020 - Dec 2022
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Role in this project:Backend Developer & MLOps Engineer
Backend Developer & MLOps EngineerContributions:128 reviews, 12 commits, 35 PRs in 2 years 2 months
Contributions summary:William's commits primarily involve modifications to CUDA-accelerated algorithms and primitives for machine learning within the RAFT library. They made changes related to distance calculations and the SVD solver, adapting code to support different CUDA versions and address solver limitations. The user also contributed to DevOps by specifying branch versions for dependencies in the build script and updated the code to avoid deprecated functions related to cuSparse. These changes demonstrate expertise in both backend development, especially in CUDA-accelerated libraries, and MLOps practices by adapting the build process to address versioning issues.
sciencedata-sciencemachine-learninggraph-data-sciencefundamental
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