Bobby Wang
Distributed System Software Engineer at NVIDIA
China
Summary
🤩
Rockstar🎓
Top SchoolBobby Wang is a distributed systems software engineer with 11 years of experience building high-performance data and GPU-accelerated systems at NVIDIA. He has deep backend and test-automation expertise, contributing Java bindings and unit tests to rapidsai/cudf and enhancing NVIDIA's spark-rapids plugin to broaden GPU support and integrate Alluxio for faster file I/O. His contributions to Apache Spark and XGBoost show a practical focus on data skew resolution, resource-aware scheduling (including fractional GPUs), and robust model/integration behavior under production conditions. Prior roles at Qualcomm, Intel, and Huawei grounded him in low-level Linux drivers and Android framework work, giving him a rare blend of systems-level and data-platform experience. Based in China, he combines rigorous testing discipline with performance-minded engineering to push cutting-edge open-source data tooling into production.
10 years of coding experience
7 years of employment as a software developer
Bachelor's Degree, Communication and Information System, Bachelor's Degree, Communication and Information System at Southwest University of Science and TechnologyMaster's Degree, Communication and Information System, Master's Degree, Communication and Information System at University of Electronic Science and Technology
Github Skills (30)
unit-testing10
spark-sql10
xgboost10
apache-spark10
c-language10
data-engineering10
testing10
gpu-programming10
big-data10
machine-learning10
cudf10
java10
scala10
javas10
rapids10
Programming languages (10)
Github contributions (5)
NVIDIA/spark-rapids
May 2020 - Nov 2022
Spark RAPIDS plugin - accelerate Apache Spark with GPUs
Role in this project:
Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:302 reviews, 65 commits, 125 PRs in 2 years 5 months
Contributions summary:Bobby primarily focused on improving the `spark-rapids` plugin, which accelerates Apache Spark with GPUs. Their contributions included fixing issues related to data type handling in GPU operations like casting and windowing functions. They also added support for Alluxio integration to accelerate file reading and extended support for various features such as handling byte, short, int and long values and for array operations to increase the range of supported functions within the plugin.
gpu-accelerationgpuapachebig-dataspark
dmlc/xgboost
Aug 2019 - Jan 2023
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:389 reviews, 67 commits, 144 PRs in 3 years 5 months
Contributions summary:Bobby contributed to the model parameter recovery process and implemented and improved core functionalities within the `xgboost` library. Their work included modifying source code related to model loading and configuration, particularly within the `learner.cc` and `test_learner.cc` files. They also added features, validated feature sizes, and resolved comments in the `jvm-packages`, which included changes to the `xgboost4j-spark` test files, improving its integration and functionality.
xgboostpythonflinkdaskdataflow
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