Matan Perlmutter
Software Engineer at Monocle
Israel
Summary
🤩
Rockstar🎓
Top SchoolMatan Perlmutter is a software engineer with 11 years of experience building backend systems and ML validation tooling, currently working at Monocle and previously a core contributor at Deepchecks. He helped implement model performance analysis, feature-importance logic, and data-type handling for Deepchecks’ open-source continuous ML validation framework, demonstrating strong data-science-informed engineering. His background includes leading software teams in the Israel Defense Forces, where he combined hands-on backend development with people management. Comfortable across production backends and ML-centric tooling, he focuses on shipping reliable, testable systems that bridge research and production. Based in Israel, he holds a BSc in Computer Science and brings a practical, security-conscious mindset forged in high-responsibility operational environments.
11 years of coding experience
9 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at The College of Management Academic Studies
Github Skills (19)
data-validation10
python10
evaluation10
data-science10
pandas10
machine-learning10
eval10
model-validation10
data-analysis10
application-monitoring9
monitoring9
pytorch9
scikit9
performance-monitoring9
scikit-learn9
Programming languages (8)
Github contributions (5)
deepchecks/deepchecks
Oct 2021 - Jan 2023
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:647 reviews, 268 commits, 396 PRs in 1 year 3 months
Contributions summary:Matan's commits primarily involve modifications to the `mlchecks` project, focusing on the development of new features and enhancements. Key contributions include implementing and refining functionality related to model performance analysis and feature importance. The user also added and improved core components, such as a mechanism for handling different data types, along with updates for the visualization of results in the existing framework. The commits demonstrate a strong understanding of data science concepts.
aimachine-learningmlmodelmlmodel-validation
matanper/my-hammerspoon
Jul 2020 - Jul 2025
Contributions:20 pushes, 1 branch in 5 years
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