Yanan Cao
Tech Lead Manager at Meta
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYanan Cao is a Tech Lead Manager based in Mountain View with 11 years of experience building and shipping production ML compiler and framework infrastructure. Currently leading work on the PyTorch compiler at Meta AI, she brings deep systems and hardware-aware expertise from prior roles at Google Brain (TensorFlow/MLIR/XLA/TPU) and GPU architecture internships at NVIDIA. Her background combines a PhD in Computer Engineering with hands-on contributions to core open-source ML tooling—most notably enhancements to the widely used PyTorch library such as IValue conversion logic and upsampling primitives. Known for bridging research and engineering, she focuses on robust, performance-sensitive implementations that move models from prototypes to scalable deployment.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Engineering, Doctor of Philosophy - PhD, Computer Engineering at Iowa State UniversityBachelor's degree, Electrical Engineering Technologies/Technicians, Bachelor's degree, Electrical Engineering Technologies/Technicians at Fudan University
Chinese, English
Github Skills (8)
neural-network10
pytorch10
machine-learning10
tensor10
deep-learning10
python10
autograd9
gpu9
Programming languages (6)
Github contributions (5)
pytorch/pytorch
Jun 2020 - Jun 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:1009 reviews, 89 commits, 214 PRs in 2 years
Contributions summary:Yanan primarily contributed to the PyTorch library, focusing on enhancements and additions related to machine learning and deep learning functionalities. They added and debugged code related to IValue conversion logic, contributing to improvements in the testing and functionality of the library. Furthermore, they added a meta function for upsampling bilinear2d_aa, contributing to upsampling operations in the library. The user's work suggests a focus on core functionality and improving the library's capabilities for machine learning tasks.
gpu-accelerationneural-networkpythonautogradgpu
gmagogsfm/pytorch-1
Aug 2021 - Oct 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:166 pushes, 38 branches in 4 years 3 months
pythongpu-accelerationdeep-learninggpuacceleration
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.