Jianghai Hu
Senior Software Engineer at Crypto.com
Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolJianghai Hu is a senior software engineer based in Hong Kong with five years of experience building backend systems, specializing in Go, RDBMS, and distributed system design. He prioritizes reliability and thoughtful technical/product trade-offs over rushed delivery, and consistently contributes beyond his own tasks through design reviews, code review, and team infrastructure improvements. His background includes production work at Tencent and Shopee and current backend engineering at Crypto.com, giving him exposure to high-scale payment and e-commerce systems. Jianghai also contributes to prominent open-source projects like PaddlePaddle, where he implemented FLOPs calculations and device/cluster partitioning features—an indication of his comfort with performance-sensitive, distributed computation. He pairs rigorous engineering discipline with a collaborative mindset and a habit of validating designs with tests and measurable metrics.
5 years of coding experience
4 years of employment as a software developer
graduate, Computer Science, graduate, Computer Science at The George Washington UniversityBachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at Xiamen University
Github Skills (8)
unit-testing10
paddlepaddle10
machine-learning10
deeplearning-ai10
deep-learning10
python10
neural-network9
distributed-training9
Programming languages (4)
Github contributions (5)
PaddlePaddle/Paddle
Nov 2022 - Jan 2023
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:
ML Engineer
ML EngineerContributions:5 commits, 11 PRs, 1 comment in 1 month
Contributions summary:Jianghai focused on implementing and verifying the computational cost (FLOPs) of various deep learning operations within the PaddlePaddle framework. They added FLOPs calculations for new operations such as `matmul`, `c_embedding`, `conv2d`, and `pool`, as well as for existing operations such as `relu`, `elu`, `leaky_relu`, `prelu`, `relu6`, and `silu`. Additionally, they added unit tests to validate these FLOPs calculations. They also added cluster partition and device meshes to process_meshes funcs.
pytorchpythonparalleldeep-learningpaddlepaddle
CjhHa1/Paddle
Nov 2022 - Feb 2023
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）
Contributions:72 pushes, 8 branches in 2 months
pytorchparalleldeep-learningreinforcement-learningindustrial
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