Shawn Lu
Software Engineer at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShawn Lu is a software engineer with nine years of experience building high-impact consumer products and developer-facing SDKs, currently working on Android native product engineering at Google in Mountain View. He has a strong Android background from multiple roles at NortonLifeLock where he shipped features for a flagship app with 800K+ installs and led Node.js API work powering the mobile experience. Shawn pairs mobile product engineering with deep infrastructure and ML deployment expertise—his open-source contributions touch TensorFlow, TensorFlow Serving, and TensorFlow Federated, including remote executor gRPC stubs, TPU validation checks, and CUDA/Python build upgrades. That blend of client-facing Android development and low-level ML serving/devops work gives him an unusual cross-stack fluency from device UX to distributed model execution. Trained in electrical engineering at Wuhan University and UPenn, he brings both rigorous systems thinking and practical production experience to complex, large-scale systems.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering - BE, Electrical Engineering, Bachelor of Engineering - BE, Electrical Engineering at Wuhan UniversityMaster of Science - MS, Electrical Engineering, Master of Science - MS, Electrical Engineering at University of Pennsylvania
English, Chinese
Github Skills (21)
docker10
python10
machine-learning10
mlr10
dockers10
tpu10
cicd10
deep-learning10
tensorflow10
remote-execution10
grpc10
unit-testing9
gpu9
serve9
cuda9
Programming languages (2)
Github contributions (5)
tensorflow/serving
Mar 2023 - Jan 2024
A flexible, high-performance serving system for machine learning models
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:1 release, 4 reviews, 34 PRs in 10 months
Contributions summary:Shawn's primary contributions involve modifying the Dockerfile and build configuration related to TensorFlow Serving. They upgraded CUDA versions, integrated Python 3.8 and 3.9, and addressed dependencies related to the "tensorflow-gpu" package. Their work also includes adjustments to the `setup.py` file related to TensorFlow versions and GPU builds. This indicates a focus on infrastructure, build processes, and environment management to support the serving of machine learning models.
cpppythonservingdeep-learningml
google-parfait/tensorflow-federated
Jan 2022 - May 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:13 commits, 1 PR in 3 months
Contributions summary:Shawn contributed to the development of the TensorFlow Federated framework by implementing a helper class for representing data-yielding computations and its associated test suite. They also added a stub interface for remote execution and implemented a concrete gRPC stub. Furthermore, the user refactored the remote executor to be parameterized with the remote_executor_stub and added executor factories to the framework. This indicates involvement in both the core logic of the framework and its deployment/execution infrastructure.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
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