Robert Xiu
QA Engineer at AMD
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolRobert Xiu is a QA engineer and mathematician-computer scientist from the University of Toronto with 11 years of hands-on software experience across firmware validation, backend systems, and ML infrastructure. He currently validates platform firmware at AMD while contributing to high-profile open-source projects like PyTorch, where he improved tensor and convolution meta-tensor functionality and data-processing utilities. Robert has built production services and APIs (Java Spring Boot, Python, AWS) that scaled trading infrastructure and sped up core algorithms by 500%, and has experience optimizing CI/CD and model-serving pipelines. His background spans full-stack feature work, low-level C and embedded-focused testing, and research-grade computer vision and ConvNet development. Notably, he combines rigorous mathematical thinking with practical engineering—writing compilers for teaching, creating curriculum, and shipping deployable systems.
11 years of coding experience
4 years of employment as a software developer
HBSc, Mathematics and Computer Science, 4.00, HBSc, Mathematics and Computer Science, 4.00 at University of Toronto
Chinese, English
Github Skills (12)
neural-network10
pytorch10
machine-learning10
tensor10
python10
data-pipelines9
autograd9
data-pipeline9
deeplearning-ai9
deep-learning9
numpy8
gpu7
Programming languages (15)
Github contributions (5)
pytorch/pytorch
Jun 2022 - Aug 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:52 reviews, 11 commits, 6 PRs in 2 months
Contributions summary:Robert primarily contributed to the PyTorch codebase by adding and improving functionalities within the data processing and core tensor manipulation modules. Their work involved implementing a functional API for file listing, enhancing the `random_split` function to support fractional splits, and adding validation for mapper functions. Furthermore, the user worked on shape calculations for meta tensors within the convolution modules, specifically for `Conv2d`, thereby contributing to the improvements in the automatic differentiation and meta-tensor capabilities of PyTorch.
pythongpu-accelerationdeep-learninggpunumpy
approx-ml/approx
Jul 2022 - Aug 2022
Automatic quantization library
Contributions:15 reviews, 28 commits, 14 PRs in 1 month
nlppythondeep-learningmachine-learningneural-network
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