Lei Wang
Performance Architect at Alibaba Group
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolLei Wang is a Performance Architect with 10 years of experience building high-performance infrastructure, simulators, and scalable backend systems across companies like Alibaba, Baidu, Micron, and Yahoo. He combines deep academic training (PhD in Computer Engineering) with hands-on expertise in cycle-accurate performance simulation, CPU performance modeling, and containerized CI/CD and deployment for complex systems. Lei has driven production-grade automation and DevOps improvements in high-profile open-source projects such as Apollo (autonomous driving) and PaddlePaddle, focusing on build systems, Docker/Bazel integration, and test automation. He’s comfortable bridging low-level performance modeling in C++ with cloud-native service delivery, having launched simulation services on Azure and Baidu Cloud and implemented OTA systems for vehicles. An unusual strength is his track record of translating academic cycle-accurate research into practical tooling and CI workflows that accelerate real-world deployment.
9 years of coding experience
12 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Beijing Normal UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Peking UniversityDoctor of Philosophy (Ph.D.), Computer Engineering, Doctor of Philosophy (Ph.D.), Computer Engineering at Texas A&M University
English, Chinese
Github Skills (14)
bash10
docker10
ros10
dockers10
build-automation10
cicd10
autonomous-driving10
bazel9
paddlepaddle9
python8
machine-learning4
deep-learning4
deeplearning-ai4
distributed-training3
Programming languages (2)
Github contributions (5)
ApolloAuto/apollo
Jul 2017 - Aug 2019
An open autonomous driving platform
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:656 commits, 193 PRs, 34 pushes in 2 years 1 month
Contributions summary:Lei's contributions primarily involved modifications to the build and deployment environment of the Apollo autonomous driving platform. They added dependencies for ROS packages, refactored docker scripts, updated docker images, and fixed build configurations. Moreover, the user integrated and maintained core system functionalities of the repository by adding and refactoring the code for building and testing. The user also moved ROS package dependencies to a Bazel WORKSPACE file and made adjustments related to the project's build process, along with fixes for camera and GPS device handling, reflecting a focus on infrastructure and integration.
autonomousmachine-learningautonomyautonomous-drivingapollo
PaddlePaddle/Paddle
Apr 2018 - Jun 2018
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:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:30 commits, 56 PRs, 22 pushes in 2 months
Contributions summary:Lei focused on improving the build and CI/CD processes for the PaddlePaddle repository. Their contributions include refactoring and improving shell scripts related to building, testing, and Docker image generation. They introduced features such as running single tests, caching for faster builds, and enhanced the overall automation workflow. The user also addressed style issues and refactored build scripts.
pytorchpythonparalleldeep-learningpaddlepaddle
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