Jerry Peng
Software Developer at Amazon
City of Ithaca, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJerry Peng is a software developer at Amazon with six years of experience building full‑stack services, RESTful APIs, and data-driven applications, grounded in a dual BA in Computer Science and Economics from Cornell. He has shipped APIs to streamline datalake onboarding and schema evolution at Amazon and contributed ML-focused features to the high-profile aws/sagemaker-python-sdk, enhancing model registration and repackaging workflows. Comfortable across Java, C#, .NET, and Python, he blends backend engineering with practical machine learning work—having improved a client’s income-detection model and processed millions of financial transactions during an earlier data science role. Jerry’s background spans finance, research, and teaching, reflecting a habit of turning complex data and domain constraints into reliable, testable systems. Based in Ithaca, he brings both product-minded pragmatism and a curiosity for applying CS to financial challenges.
6 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA, Computer Science & Economics, Graduated alum, Bachelor of Arts - BA, Computer Science & Economics, Graduated alum at Cornell UniversityThe Harker School
Github Skills (10)
work-flow10
machine-learning10
workflow-engine10
workflow-automation10
aws10
python10
sagemaker10
pytorch8
pytest8
tensorflow8
Programming languages (5)
Github contributions (5)
aws/sagemaker-python-sdk
May 2021 - Dec 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:171 reviews, 25 commits, 43 PRs in 1 year 7 months
Contributions summary:Jerry contributed to the `aws/sagemaker-python-sdk` repository by implementing and modifying functionalities related to SageMaker workflows. Their work focused on enhancing the `RegisterModel` step, including custom dependency support, and on integrating model repackaging. These modifications included adjustments to the test suite and the introduction of new features for model registration and transformation.
amazon-sagemakermachine-learning-modelsawsmxnettensorflow
aws-sagemaker-mlops/sagemaker-model-building-pipeline-definition-JSON-schema
Aug 2021 - Dec 2022
Amazon SageMaker Model Building Pipelines is a tool for building machine learning pipelines that take advantage of direct SageMaker integration. Because of this integration, you can create a pipeline and set up SageMaker Projects for orchestration using a tool that handles much of the step creation and management for you. This repository hosts the pipeline definition JSON schema
Contributions:1 review, 1 PR, 32 pushes in 1 year 4 months
amazon-sagemakerjson-schemamachine-learningsagemaker
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