Michael Garod
Software Engineer at Zillow
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolMichael Garod is a software engineer with 11 years of experience building production systems across startups and large enterprises, currently focused on backend work at Zillow. He brings deep ML and cloud experience from IBM—contributing to AutoAI, Deep Learning-as-a-Service, and handwriting recognition—and has hands-on expertise deploying containerized workloads on Kubernetes. At Cedar he shipped full-stack features that drove large-scale patient communications, and earlier work at Barclays involved ultra-low-latency C/C++ tooling for FPGA-driven trading. Michael is passionate about Python and practical ML tooling—he contributed to the Yellowbrick visualization library by improving silhouette diagnostics, tests, and docs—and is comfortable across Golang, C++, Docker, Ansible, MongoDB, and AWS. A former adjunct lecturer in computer science, he blends research-grade ML development with a talent for explaining complex concepts to diverse audiences.
11 years of coding experience
6 years of employment as a software developer
Certificate, Classic Culinary Arts, Certificate, Classic Culinary Arts at International Culinary CenterProfessional Development, Professional Development at Beyond CodingBachelor of Arts (B.A.), Computer Science, 3.842, Bachelor of Arts (B.A.), Computer Science, 3.842 at City University of New York-Hunter College
English, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
scikit-learn10
machine-learning10
visualization10
visualizations10
python10
matplotlib10
scikit10
anaconda7
Programming languages (4)
Github contributions (5)
DistrictDataLabs/yellowbrick
May 2019 - Oct 2020
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Role in this project:
Data Scientist
Data ScientistContributions:6 reviews, 7 commits, 7 PRs in 1 year 5 months
Contributions summary:Michael primarily contributed to the Yellowbrick library by enhancing the Silhouette Visualizer. Their work included adding features like a legend for the average silhouette score, customizing colors, and repositioning cluster labels for better visualization. They also improved test coverage and image comparison within the test suite, and updated the documentation to reflect these changes. These improvements directly support the analysis and selection of machine learning models.
pythonvisual-analysisvisualizermodel-selectionmachine-learning
mgarod/Google_CS_with_Android
Jan 2016 - Feb 2016
Contributions:8 commits, 7 pushes, 1 branch in 27 days
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