Brandon Miller
Software Engineer - RAPIDS AI at NVIDIA
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolBrandon Miller is a software engineer at NVIDIA working on RAPIDS AI with six years of experience bridging enterprise data science, scientific HPC, and large-scale ML pipelines. He brings deep GPU programming and data-engineering expertise, contributing core cuDF features for efficient GPU DataFrame operations and producing CUDA examples for Numba documentation. Prior roles at Allstate ranged from data analytics engineering to senior data scientist, where he led deep learning R&D and scalable ML infrastructure. His academic background in physics and hands-on HPC support work inform a pragmatic, performance-first approach to algorithm design and optimization. Notably, he has authored reproducible Monte Carlo datasets for gravitational wave research, reflecting a history of turning complex scientific computation into production-ready tooling.
6 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.), Physics and Astronomy, Bachelor of Science (B.S.), Physics and Astronomy at Rochester Institute of TechnologyMaster of Science (M.S.), Physics, Master of Science (M.S.), Physics at Northwestern University
English
Github Skills (17)
algorithm10
algorithms10
c-language10
python10
categorical-data10
data-structure10
numpy10
compiler-compiler10
cuda10
data-structures10
compiler10
datastructures-algorithms10
numba10
cprogramming-language10
data-engineering9
Programming languages (6)
Github contributions (5)
rapidsai/cudf
Aug 2019 - Jan 2023
cuDF - GPU DataFrame Library
Role in this project:
Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:1307 reviews, 1206 commits, 228 PRs in 3 years 6 months
Contributions summary:Brandon appears to have been involved in implementing core features related to cuDF's merge functionality. Their commits focus on handling categorical variables and typecasting, as well as fixing bugs. Their work involved modifying core data structures and algorithms, specifically within the `cudf::strings` library. The user is likely contributing to the core features and optimizations.
cudadataframe-librarydata-analysiscppcudf
numba/numba
Mar 2022 - Apr 2022
NumPy aware dynamic Python compiler using LLVM
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:6 reviews, 28 commits, 1 PR in 1 month
Contributions summary:Brandon primarily contributed to documentation examples related to CUDA kernels within the Numba project. They added and modified examples, including vector addition, heat equation, shared memory reduction, and sessionization. Their work involved integrating code snippets, updating documentation, and addressing review feedback, showcasing their proficiency in using Numba for GPU programming and documentation generation. The user also demonstrated proficiency with code refactoring and testing.
cudapythonparallelnumpynumba
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