Shibo Xing
AI Framework Engineer at Baidu, Inc.
Greater Pittsburgh Region United States
Summary
🤩
Rockstar🎓
Top SchoolShibo Xing is an AI framework engineer with 8 years of experience building and optimizing deep learning infrastructure for cloud and edge environments. He has delivered production-grade PyTorch optimizations and CI/CD tooling at AWS—authoring Deep Learning Containers, machine images, and parallelized build pipelines—and now focuses on model inference and training performance at Baidu, tuning CV and LLM workloads on Kunlun GPUs. His hands-on strengths span GPU kernel programming, graph/kernel fusion, TVM-based inference runtimes, and profiling-driven bottleneck resolution, which have repeatedly reduced binary sizes and accelerated workflows. A dual-degree computer scientist from the University of Pittsburgh, he also brings quant modeling and full‑stack web experience, demonstrating an uncommon blend of low-level performance engineering and practical product delivery.
8 years of coding experience
2 years of employment as a software developer
Master's degree Computer Science (Machine Learning), Master's degree Computer Science (Machine Learning) at University of Pittsburgh
Github Skills (10)
pytorch10
docker10
aws10
dockers10
build-automation10
cicd10
sagemaker10
bash9
python8
tensorflow5
Programming languages (4)
Github contributions (5)
aws/deep-learning-containers
Sep 2022 - Jan 2023
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:
DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:104 reviews, 46 commits, 134 PRs in 4 months
Contributions summary:Shibo's commits primarily focus on building and configuring Docker images for the AWS Deep Learning Containers. They are involved in creating build specifications, managing URLs for PyTorch binaries, and modifying Dockerfiles for various PyTorch versions and configurations, including CPU and GPU setups. Furthermore, the user implements tests and adjusts configurations for SageMaker releases, indicating responsibilities in CI/CD pipeline maintenance and environment setup for different deployment targets. Their work involves fixing dependencies, handling security checks, and managing the integration with different AWS services.
pytorchsagemakercontainersmxnetserving
ShiboXing/SOTA
Dec 2021 - Feb 2025
Contributions:3 reviews, 1 PR, 65 pushes in 3 years 2 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.