Charlie Yan
Staff Machine Learning Engineer at Apple
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolCharlie Yan is a Staff Machine Learning Engineer based in Seattle with 11 years of experience designing and scaling production ML systems across top tech companies including Apple, Meta (Facebook), Amazon, and Microsoft. He specializes in distributed training, quantization, and large-scale online advertising prediction systems, moving from research roles into senior engineering and staff leadership. Charlie has contributed to PyTorch core tests and distributed/quantization features, reflecting hands-on impact on one of the most important open-source deep learning frameworks. His background blends academic rigor from Peking University with practical delivery of high-throughput CVR and CTR models and parallel LR training systems. Known for improving the reliability and usability of distributed training stacks, he bridges ML research and production engineering. A subtle strength is his history of evolving roles across major platforms, giving him rare end-to-end insight from algorithm to serving at scale.
11 years of coding experience
17 years of employment as a software developer
Bachelor's Degree, Computer Science, Bachelor's Degree, Computer Science at Beihang UniversityMaster's Degree, Computer Science, Master's Degree, Computer Science at Peking University
Github Skills (12)
quantization10
pytorch10
machine-learning10
deeplearning-ai10
distributed-training10
deep-learning10
python10
gpu9
neural-network9
tensor8
autograd8
numpy7
Programming languages (10)
Github contributions (5)
pytorch/pytorch
Jun 2022 - Dec 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:71 reviews, 199 commits, 47 PRs in 5 months
Contributions summary:Charlie's commits primarily focus on enabling and testing distributed algorithms and quantization features within the PyTorch framework. They are actively involved in modifying test scripts and adding new tests related to distributed training, quantization, and DDP (DistributedDataParallel). The user's work includes fixing documentation and formatting code related to the DDP module, demonstrating a focus on improving the usability and functionality of PyTorch's distributed training capabilities. The contributions cover a variety of aspects of distributed deep learning, including DDP, and quantization.
pythongpu-accelerationdeep-learninggpunumpy
yhcharles/pracode
Oct 2017 - May 2018
Contributions:22 commits, 9 pushes, 5 branches in 6 months
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.