Zhenhuan Liu

Software Engineer at 英伟达

Beijing, China
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

🤩
Rockstar
🎓
Top School
Liu Zhenhuan is a software engineer based in Beijing with nine years of experience focused on generative image modeling and computer vision. Currently at NVIDIA, he contributes to Megatron-Core MoE development, blending research-grade modeling techniques with production-scale engineering. He holds a master's in Computer Vision from the Institute of Computing Technology, Chinese Academy of Sciences and a bachelor's in Software Engineering from Nankai University, grounding his work in both strong academic research and practical software design. Known for bridging prototype research with scalable implementation, he brings deep expertise in large-model architectures and a track record of shipping complex ML systems at scale.
code9 years of coding experience
bookBachelor's degree, Software Engineering, Bachelor's degree, Software Engineering at 南开大学
bookMaster of Computer Applications - MCA, Computer Vision, Master of Computer Applications - MCA, Computer Vision at Institute of Computing Technology, Chinese Academy of Sciences
github-logo-circle

Github Skills (82)

transformers10
python10
diffusion-models10
text-to-speech10
automatic-speech-recognition10
transformer-models10
deep-learning10
image-generation10
speech10
gpu10
floating-point10
gpu-acceleration10
machine-translation10
hydra10
multimodal10

Programming languages (8)

TypeScriptJavaScriptVueGoObjective-CJupyter NotebookVim ScriptPython

Github contributions (5)

github-logo-circle
Victarry/dotfiles

Aug 2020 - Dec 2023

:wrench: .files, including ~/.macos — sensible hacker defaults for macOS
Contributions:33 pushes, 1 branch, 9 comments in 3 years 4 months
macos
Victarry/TransformerEngine

Nov 2023 - Apr 2026

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
Contributions:56 pushes, 8 branches in 2 years 5 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.
Request Free Trial