Ryan Lee
AI Infrastructure Software Engineer at NVIDIA
Cupertino, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRyan Lee is an AI Infrastructure Software Engineer with 11 years of experience building high-performance data processing systems, currently driving GPU-accelerated infrastructure at NVIDIA from Cupertino. He has deep expertise in CUDA-driven back-end development, contributing to prominent open-source projects like rapidsai/cudf and NVIDIA/spark-rapids where he optimized dataframe merging, string operations, hashing (including MD5 and Murmur3), and complex type casting for Spark. Prior roles at Yahoo exposed him to large-scale ML platforms and production systems, grounding his work in practical, production-ready performance improvements. Known for tackling low-level kernel optimizations and byte-casting challenges, he blends systems-level rigor with a focus on accelerating data-intensive workloads. His background from the University of Rochester and long-tenured contributions to industry-grade GPU tooling make him a go-to engineer for squeezing performance out of modern AI stacks.
11 years of coding experience
2 years of employment as a software developer
De Anza CollegeBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at University of Rochester
Github Skills (25)
decimals10
spark10
c-language10
gpu-programming10
bigdecimal10
cudf10
decimal-numbers10
cuda10
hashing-algorithm10
cpp10
cprogramming-language10
decimal10
data-structure9
strings9
algorithm9
Programming languages (10)
Github contributions (5)
NVIDIA/spark-rapids
Oct 2020 - Jan 2023
Spark RAPIDS plugin - accelerate Apache Spark with GPUs
Role in this project:
Back-end Developer
Back-end DeveloperContributions:37 reviews, 22 commits, 39 PRs in 2 years 3 months
Contributions summary:Ryan primarily focused on enhancing the `spark-rapids` project by implementing and refining features related to data type casting and hash functions. They contributed code to support casting for `BinaryType` and implemented a `GpuMurmur3Hash` function for Spark SQL. Additionally, the user provided support for decimal data types in addition and subtraction operations, and struct to string casting. Furthermore, the user improved support for Iceberg and union operators.
gpu-accelerationgpuapachebig-dataspark
rapidsai/cudf
Jul 2019 - Aug 2022
cuDF - GPU DataFrame Library
Role in this project:Back-end Developer
Back-end DeveloperContributions:140 reviews, 24 commits, 37 PRs in 3 years 1 month
Contributions summary:Ryan primarily contributed to the `cudf` library, focusing on core functionality within the merge, text, and hashing modules. They implemented and optimized code related to merging dataframes, string manipulation, and MD5 hashing. Furthermore, the user worked on byte casting functionalities. The user's work involved changes within CUDA kernels, indicating significant experience in GPU-accelerated data processing.
cudadataframe-librarydata-analysiscppcudf
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