Payton Staub
Software Development Manager at Amazon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolPayton Staub is a Software Development Manager in Seattle with 11 years of experience building scalable ML and backend systems, currently leading teams on Amazon SageMaker. He progressed from SDE2 to manager at SageMaker, combining hands-on engineering with people leadership to ship pipeline, model-building, and deployment features at cloud scale. His background in electrical and wireless engineering and a history as a startup founder give him a systems-level view that bridges hardware-aware thinking with large-scale cloud software. Active in open source, he contributed to the widely used aws/sagemaker-python-sdk to make pipelines more flexible (processor types, parameterized TF hyperparameters, and callback steps). Known for pragmatic delivery, he pairs deep backend and MLOps expertise with experience in microservices and front-end stacks from earlier roles.
11 years of coding experience
12 years of employment as a software developer
Bachelors of Science, Electrical Engineering, Wireless Engineering, Business, Bachelors of Science, Electrical Engineering, Wireless Engineering, Business at Auburn University
Stackoverflow
582reputation
21kreached
22answers
6questions
Github Skills (17)
python10
machine-learning10
pipelining10
sagemaker10
tensorflow10
aws10
pipe10
pipeline10
pytorch9
cicd8
amazon-sagemaker-studio6
jupyter-notebook6
amazon-sagemaker6
aws-cloudformation6
amazon-web-services6
Programming languages (3)
Github contributions (5)
aws/sagemaker-python-sdk
Mar 2021 - Apr 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:121 reviews, 25 commits, 31 PRs in 1 year
Contributions summary:Payton contributed to the `aws/sagemaker-python-sdk` repository by implementing features to support different processor types within the ProcessingStep. They worked on allowing hyperparameters in the Tensorflow estimator to be parameterized for a pipeline. Additionally, the user was involved in adding support for Callback steps in model building pipelines. The commits demonstrate the user's work in enhancing the flexibility and functionality of the SageMaker SDK, particularly concerning pipeline creation and model management.
pytorchsagemakerdeployingmxnetpython
staubhp/sagemaker-python-sdk
Mar 2021 - Dec 2021
A library for training and deploying machine learning models on Amazon SageMaker
Contributions:55 pushes, 18 branches in 9 months
deployingsagemakeramazon-sagemakerdeploying-machine-learningamazon
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