Shijie Ding
Design Verification Engineer
Westford, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolShijie Ding is a design verification engineer with nine years of experience building and validating complex hardware and system designs, currently contributing at NVIDIA from Westford, MA. He holds a B.S. from Tsinghua and a 4.0 M.Eng. in ECE from Cornell, blending rigorous academic training with practical industry delivery at Oracle and NVIDIA. His background spans hardware engineering through senior roles into verification, with hands-on expertise in test automation and debug workflows. Outside core ASIC work he contributes to the well-known PaddlePaddle open-source deep learning project, improving unit test robustness and distributed-test stability—an indicator of his attention to reliability across software and hardware boundaries. Practical, detail-oriented, and cross-disciplinary, he excels at closing the loop between design, verification, and production-quality testing.
9 years of coding experience
3 years of employment as a software developer
Master's degree, Electrical and Computer Engineering, 4.0, Master's degree, Electrical and Computer Engineering, 4.0 at Cornell UniversityBachelor of Science (B.S.), Electrical Engineering and Automation, Bachelor of Science (B.S.), Electrical Engineering and Automation at Tsinghua University
Stackoverflow
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Github Skills (14)
neural-network10
unit-testing10
paddlepaddle10
machine-learning10
deeplearning-ai10
deep-learning10
distributed-training10
python10
test-automation10
cuda7
c-language4
pytorch4
cprogramming-language4
tensorflow4
Programming languages (3)
Github contributions (5)
PaddlePaddle/Paddle
Jun 2022 - Dec 2022
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
ML Engineer & QA Engineer / Test Automation Engineer
ML Engineer & QA Engineer / Test Automation EngineerContributions:29 reviews, 6 commits, 40 PRs in 5 months
Contributions summary:Shijie primarily contributed to the PaddlePaddle repository by fixing unit test failures and improving the test suite. Their work involved identifying and resolving errors in various unit tests, including those related to `fuse_resnet_unit`, `imperative_auto_mixed_precision`, and `sparse_attention_op`. They also addressed issues related to random seeds within the unit tests for distributions and further optimized testing procedures by updating distributed strategy and fixing fuse gemm epilogue.
pytorchpythonparalleldeep-learningpaddlepaddle
Wong4j/Paddle
Jun 2022 - Dec 2024
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Contributions:221 pushes, 38 branches in 2 years 6 months
pytorchparalleldeep-learningreinforcement-learningindustrial
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