Katharine Yang
General Ledger Accountant at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKatharine Yang is a seasoned General Ledger Accountant in the San Jose Bay Area with over 15 years of corporate finance experience and a strong GAAP foundation, currently managing GL functions at NVIDIA. She brings deep month-end close, payroll reconciliation, intercompany and SOX compliance expertise honed across Fortune 500 and multinational environments. Known for being trustworthy, direct, and deadline-driven, she partners effectively with auditors and cross-functional teams to resolve complex accounting issues. In addition to accounting, she has contributed technical improvements to high-profile open-source projects like NVIDIA’s Triton Inference Server, helping modernize client libraries and support inference backends—an unusual blend of accounting rigor and hands-on engineering collaboration. This combination of financial stewardship and practical engineering involvement enables her to bridge technical and finance teams when operationalizing ML and infrastructure investments.
5 years of coding experience
5 years of employment as a software developer
B.S., Business Administration in Accounting, B.S., Business Administration in Accounting at California State University-Stanislaus
Github Skills (24)
client-library10
c-language10
docker10
python10
inference10
dockers10
cicd10
numpy10
triton10
tensorflow10
triton-server10
cprogramming-language10
conda9
bash9
grpc8
Programming languages (5)
Github contributions (5)
triton-inference-server/client
Mar 2021 - Jan 2023
Triton Python, C++ and Java client libraries, and GRPC-generated client examples for go, java and scala.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:108 reviews, 28 commits, 79 PRs in 1 year 10 months
Contributions summary:Katharine primarily focused on refactoring and improving the client-side libraries for the Triton Inference Server. Their contributions involved updating deprecated NumPy usages, removing namespace dependencies, and implementing changes to support the C API for the performance analyzer. The user also addressed issues related to caching and improved the robustness of the client. The modifications span across Python, C++, and Java client libraries.
golangpythongrpcscalaclient-libraries
triton-inference-server/server
Mar 2021 - Jan 2023
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Role in this project:Back-end Developer
Back-end DeveloperContributions:245 reviews, 53 commits, 146 PRs in 1 year 10 months
Contributions summary:Katharine primarily contributed to client-side code improvements by removing deprecated NumPy usages. They replaced `np.bool` with `bool` and changed `npbools` to `bools` throughout the client. The changes involved modifications to files that interacted with various machine learning frameworks like TensorFlow, PyTorch, and TensorRT, showing a focus on ensuring compatibility with these frameworks. These changes streamlined the client, improving compatibility with newer NumPy versions while supporting inference server functionality.
nvidia-dockernvidiadeep-learninggpuinference
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