Zhuo Weng
Software Engineer at LinkedIn
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolZhuo Weng is a software engineer with nine years of experience building machine learning infrastructure and backend services, currently focused on ML Infra at LinkedIn after a multi-year stint at AWS. He has hands-on expertise in performance engineering for deep learning—contributing to AWS Deep Learning Containers by integrating and benchmarking PyTorch for CPU/GPU workloads and improving CI/CD and test infrastructure. Zhuo combines a strong academic foundation (MS in Computer Science from USC, BS in Electrical and Electronics Engineering) with practical skills in DevOps, benchmarking, and distributed system reliability. Based in Sunnyvale, he brings an engineer-first approach to making ML tooling reproducible and production-ready, and has a track record of automating benchmark pipelines and S3-based result analysis that aren’t always visible on a resume.
9 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Southern CaliforniaBachelor's degree, Electrical and Electronics Engineering, Bachelor's degree, Electrical and Electronics Engineering at University of Electronic Science and Technology of China
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
pytorch10
benchmark10
docker10
benchmarking10
aws10
pytest10
dockers10
testing10
machine-learning9
aws-s38
s3-bucket8
cicd8
python8
amazon-s38
tensorflow8
Programming languages (5)
Github contributions (5)
aws/deep-learning-containers
May 2020 - Aug 2020
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:
ML Engineer & DevOps Engineer
ML Engineer & DevOps EngineerContributions:15 releases, 2 reviews, 62 commits in 3 months
Contributions summary:Zhuo's primary contribution focused on integrating and benchmarking PyTorch inference and training within the AWS Deep Learning Containers. This involved adding performance tests for both CPU and GPU configurations, including synthetic and ImageNet benchmarks. They also developed scripts to upload benchmark results to S3 buckets and implemented changes to support the execution and analysis of these performance tests within the containerized environment. Moreover, the user made modifications to the testing infrastructure and CI/CD pipeline to support the benchmark executions.
pytorchsagemakercontainersmxnetserving
YYStreet/deep-learning-containers
May 2020 - Aug 2020
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Contributions:28 PRs, 506 pushes, 155 branches in 3 months
containerspytorchmxnetservingcaffe2
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