Brian Wieder
Software Engineer at Google DeepMind
Richmond, Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolBrian Wieder is a software engineer with a decade of experience building production systems and consumer apps, currently at Google DeepMind after several engineering roles within Google. A Virginia Tech computer science graduate, he ships end-to-end software from iOS apps (three on the App Store using Swift and React Native) to cloud-backed web services built with React, DynamoDB, and AWS Lambda. His open-source contributions show deep systems chops—improving build toolchains, CUDA Clang integration, and NCCL fixes in high-profile ML repos like TensorFlow and XLA to enhance GPU/accelerator build reliability. He pairs low-level build and DevOps experience with full-stack product instincts, comfortable tackling compiler compatibility one day and user-facing UI the next. Based in Richmond, VA, he leverages teaching and internship experience to communicate complex technical issues clearly across teams. Notably, his work on CUDA toolchains reflects a rare blend of applied ML infrastructure and practical engineering that smooths the path from research code to reproducible builds.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Virginia Tech College of EngineeringClover Hill High School
Stackoverflow
33reputation
934reached
1answer
2questions
Github Skills (19)
machine-learning10
build-system10
tensorflow10
bazel10
toolchain10
ccl10
cuda10
compiler10
clangd10
clang10
c-language9
python9
deep-learning9
cprogramming-language9
deep-neural-networks8
Programming languages (10)
Github contributions (5)
tensorflow/tensorflow
Dec 2022 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:
ML Engineer
ML EngineerContributions:4 reviews, 18 commits, 2 PRs in 1 month
Contributions summary:Brian's contributions focused on enhancing the TensorFlow library, specifically in areas related to CUDA and the integration of CUDA Clang. They modified build configurations and dependencies, including creating a toolchain for building with CUDA Clang. Additionally, the user modified build files for NCCL and removed support for CUDA on MacOS, indicating involvement in GPU-accelerated machine learning and optimization for specific hardware configurations. These changes involved modifications to build definitions and Dockerfile configurations for the Tensorflow repository.
pythondata-sciencedeep-learningmlmachine-learning
openxla/xla
Dec 2022 - Jan 2023
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:Backend & DevOps Engineer
Backend & DevOps EngineerContributions:5 commits in 1 month
Contributions summary:Brian's contributions primarily involve modifying build configurations and toolchains within the XLA project, a machine learning compiler. They added include paths for compiler resources, and updated build files for NCCL, specifically addressing compatibility issues and MacOS support. The user also created a toolchain for building with CUDA Clang and patched NCCL to resolve a compilation issue related to C++17. These changes improve build processes, and compiler compatibility.
compilercommunity-drivenmachine-learningmodular
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