Ting Lu
Senior II Deep Learning Software Engineer at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolTing Lu is a Senior II Deep Learning Software Engineer at NVIDIA with a strong focus on PyTorch core development and quality engineering. In just a few years she progressed from system software roles to senior positions driving reliability across deep learning frameworks and GPU-accelerated workloads. She combines an MS in Computer Science from the University of Chicago with a BS in Economics from Duke, blending quantitative thinking and systems intuition. Ting’s open-source contributions to the flagship pytorch/pytorch repository highlight her attention to cross-platform test robustness and hardware-aware debugging (including ARM and CUDA test tuning). Based in California, she is passionate about turning data and infrastructure complexity into dependable tools that accelerate research and production ML.
2 years of coding experience
6 years of employment as a software developer
Beijing No.171 High SchoolBachelor of Science (BS), Economics (Finance Concentration), Minor in Computer Science, Bachelor of Science (BS), Economics (Finance Concentration), Minor in Computer Science at Duke UniversityMaster in Science (MS), Computer Science, Master in Science (MS), Computer Science at University of ChicagoSemester Exchange, Semester Exchange at Duke in Venice
English, Chinese, Italian
Github Skills (10)
cuda10
pytorch10
python10
test-automation10
testing10
arm9
gpu8
machine-learning8
neural-network7
deep-learning7
Programming languages (3)
Github contributions (5)
pytorch/pytorch
Aug 2023 - Apr 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:33 reviews, 68 PRs, 181 comments in 1 year 7 months
Contributions summary:Ting primarily contributed to the quality assurance of the PyTorch library by identifying and addressing issues related to testing on various platforms and hardware configurations. Their work involved disabling failing tests related to specific hardware (e.g., ARM) or CUDA versions (e.g., SM90), often by adding skip conditions or modifying test files. They also corrected test configurations, such as fixing the world size for distributed tests and ensuring accurate skip conditions for c10d tests.
pythongpu-accelerationdeep-learninggpunumpy
tinglvv/pytorch
Aug 2023 - Mar 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:233 pushes, 67 branches in 1 year 6 months
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