Qingzi Lan
Software Engineer at Google
Fremont, California, United States
Summary
🤩
Rockstar🎓
Top SchoolQingzi Lan is a software engineer with four years of professional experience building firmware, hardware-aware software, and cloud-scale systems, currently at Google after a multi-year tenure at AWS. She has a strong foundation in computer architecture, operating systems, and distributed systems, with hands-on contributions to high-profile open-source AWS projects such as the SageMaker Python SDK and Deep Learning Containers—work that included PyTorch and Hugging Face integrations and adding Graviton support. Qingzi combines system-level rigor from ASIC verification and embedded work with practical DevOps skills, enabling her to bridge ML deployment, containerization, and low-level hardware concerns. Known as a quick learner and effective collaborator, she repeatedly leads cross-team efforts and mentors junior engineers to move complex evaluations forward.
4 years of coding experience
9 years of employment as a software developer
University of California, San DiegoDiablo Valley College
English, Chinese
Github Skills (19)
pytorch10
docker10
python10
testing10
dockers10
cicd10
sagemaker10
tensorflow10
aws10
machine-learning9
k8
k8s8
huggingface8
kubernetes-pods8
aws-ecs8
Programming languages (2)
Github contributions (5)
aws/deep-learning-containers
Aug 2021 - Oct 2022
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
DevOps EngineerContributions:164 reviews, 84 commits, 199 PRs in 1 year 1 month
Contributions summary:Qingzi's contributions primarily focused on integrating and expanding the AWS Deep Learning Containers project to support Graviton processors. They modified build specifications, Dockerfiles, and test configurations to accommodate the new architecture. The user also implemented and updated tests across various environments, including EC2, ECS, and EKS, ensuring the containers' functionality. Furthermore, they addressed issues related to package installations, especially around dependencies like protobuf and OpenSSH.
pytorchsagemakercontainersmxnetserving
aws/sagemaker-python-sdk
Mar 2022 - Aug 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 8 PRs, 9 comments in 5 months
Contributions summary:Qingzi primarily contributed to the Amazon SageMaker Python SDK by implementing new features and addressing issues related to the integration of various machine learning frameworks and services. This included adding support for PyTorch 1.12, incorporating the Neo service in the TLV region, and addressing issues with image version selection for IOC. Their work also involved the inclusion of Hugging Face Transformers support, demonstrating a focus on integrating cutting-edge deep learning tools within the SageMaker ecosystem.
pytorchsagemakerdeployingmxnetpython
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