Bryce Huang
Software Engineer at Anyscale
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBryce Huang is a software engineer in San Francisco with 8 years of experience building full-stack and distributed systems using JavaScript, Java, Python, Rust, and C/C++. Currently at Anyscale, he contributes to cloud-native AI infrastructure and has upstream impact on the widely used Ray project by improving runtime utilities, debugging support, and dashboard UX. Bryce co-founded a startup where he led a team to launch a web testing platform and cut infrastructure costs by 50% through serverless migration, and he has practical experience optimizing image processing pipelines with C++/OpenCV. He blends strong academic foundations from National Tsing Hua University and UIUC with hands-on DevOps and backend work across React, Django, Docker, AWS/GCP, and Linux. Known for focusing on reliability and developer experience, he’s also pursuing interests in distributed systems and program analysis/security through side projects.
8 years of coding experience
3 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at University of Illinois Urbana-ChampaignBachelor of Science - BS, Computer Science, Overall GPA: 3.99/4.3, Bachelor of Science - BS, Computer Science, Overall GPA: 3.99/4.3 at National Tsing Hua University
Github Skills (10)
debugging10
debug10
ray10
devops10
python10
dashboard9
deploying8
distribute7
machine-learning7
parallel7
Programming languages (6)
Github contributions (5)
ray-project/ray
Oct 2022 - Mar 2025
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:26 reviews, 16 PRs, 1 branch in 2 years 5 months
Contributions summary:Bryce primarily contributed to the Ray project by enhancing the runtime environment and debugging capabilities. They improved the reusability of the `download_and_unpack_package` function and integrated `debugpy` for debugging Ray tasks and actors. Furthermore, the user addressed dashboard issues, including fixing text overlapping in percentage bars and adding dependencies. They also introduced an API for retrieving the cluster ID.
pythonconsistsruntimetensorflowserving
brycehuang30/ray
Mar 2023 - Apr 2025
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
Contributions:56 pushes, 10 branches in 2 years
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