Jonathan Esterhazy
Senior Software Engineer at Amazon Web Services
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJonathan Esterhazy is a Senior Software Engineer in San Francisco with 15 years of experience building scalable systems and machine learning tooling at Amazon Web Services and prior engineering and leadership roles across e-commerce and publishing. He blends backend, DevOps, and ML expertise—contributing to Amazon SageMaker SDK and examples, improving CLI workflows and deployment patterns, and demonstrating practical ML experience with topic modeling work in the widely used gensim library. Comfortable shipping production services, he has a track record of evolving developer tooling and training pipelines while fixing low-level build, import, and deployment issues that keep platforms reliable. Jonathan pairs academic study in information systems and an AI nanodegree with deep hands-on experience, including implementing UCI corpus handling and adapting Hierarchical Dirichlet Process algorithms. Colleagues describe him as a pragmatic engineer who moves fluid research prototypes into production-ready infrastructure and documentation.
15 years of coding experience
29 years of employment as a software developer
B. A., Canadian Studies, B. A., Canadian Studies at University of ManitobaArtificial Intelligence Nanodegree, Artificial Intelligence Nanodegree at UdacityMaster's Degree, Information Systems, Master's Degree, Information Systems at Athabasca University
Stackoverflow
228reputation
22kreached
5answers
0questions
Github Skills (33)
gensim10
pytest10
python10
data-science10
testing10
command-line-interface10
mxnet10
machine-learning10
gluon10
sagemaker10
deep-learning10
tensorflow10
aws10
topic-models10
natural-language-processing10
Programming languages (7)
Github contributions (5)
aws/sagemaker-python-sdk
Jan 2018 - Aug 2019
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:68 releases, 38 commits, 90 PRs in 1 year 7 months
Contributions summary:Jonathan primarily contributed to the implementation and maintenance of the SageMaker CLI, evidenced by the addition of new commands and options. They focused on features like the CLI's interaction with SageMaker training jobs and hosting endpoints. Additionally, the user fixed various issues including code formatting, import errors, and documentation warnings. Their work also involved updating versions and making build configuration changes.
pytorchsagemakerdeployingmxnetpython
aws/amazon-sagemaker-examples
Nov 2017 - Dec 2018
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:ML Engineer
ML EngineerContributions:17 commits, 18 PRs, 14 pushes in 1 year 1 month
Contributions summary:Jonathan primarily contributed to examples demonstrating the use of MXNet and Gluon for machine learning tasks within the SageMaker environment. Their work involved adding and updating Jupyter notebooks showcasing ResNet training on CIFAR-10 and MNIST classification, along with modifications to training scripts. Furthermore, the user was actively involved in integrating and refining the existing TensorFlow iris example. These updates include adjustments for deployment and hyperparameter tuning configurations within the SageMaker environment, highlighting expertise in model training and deployment within the AWS ecosystem.
pythonjupyter-notebooktrainingawssagemaker
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