Min Huang
Software Engineer at Meta
United Kingdom
Summary
🤩
Rockstar🎓
Top School
Top expert inArtificial Intelligence and Computer Vision Technologies
Min Huang is a seasoned software engineer with 11 years of experience building distributed systems and applied machine learning, currently contributing to Ads Ranking Integrity at Meta. Previously he led engineering teams at Xiaomi across fintech and consumer content products and managed ad targeting efforts at Baidu, bringing both product leadership and hands-on system design experience. Min has deep open-source involvement with PaddlePaddle—modernizing models and core framework components for Python 3 and robustness in distributed training—which underscores his expertise in large-scale ML infrastructure. He combines low-level backend and test-automation skills (e.g., optimizing NCCL broadcast and benchmark tests) with practical ML model work across CV, NLP, and speech. Based in the UK, he blends enterprise-scale delivery with a developer-first approach to reproducible ML engineering. An understated strength is his track record of porting and hardening legacy codebases to modern Python standards, enabling long-term maintainability and cross-platform deployment.
11 years of coding experience
14 years of employment as a software developer
Peking University
Github Skills (20)
distributed-training10
python10
testing10
machine-learning10
deep-learning10
neural-network10
paddlepaddle10
computer-vision10
nlp10
refactor9
automatic-differentiation9
refactoring9
cprogramming-language8
tensorflow8
tensorrt8
Programming languages (9)
Github contributions (5)
PaddlePaddle/Paddle
May 2018 - Apr 2019
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:764 commits, 287 PRs, 94 pushes in 11 months
Contributions summary:Min's contributions centered around enhancing the robustness and functionality of the `paddlepaddle/paddle` repository. They implemented various distributed training test cases and learning rate decay strategies, adding features to fluid benchmark tests, including regularization and clipping. Furthermore, the user addressed and resolved specific bugs, such as those related to the NCCLBcast process, and optimized code styles for improving efficiency and maintainability.
pytorchpythonparalleldeep-learningpaddlepaddle
PaddlePaddle/models
Aug 2018 - Apr 2019
Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models and so on.
Role in this project:ML Engineer
ML EngineerContributions:43 commits, 32 PRs, 18 pushes in 8 months
Contributions summary:Min primarily focused on porting and adapting various models within the PaddlePaddle framework to Python 3, indicating a strong focus on code modernization. Their commits involved refactoring text classification, object detection, and sequence tagging models to align with Python 3 standards. This included modifying code and adapting dependencies within the `models` repository.
nlpdeep-learningpaddlepaddlerecommendationcomputer-vision
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