Masato Naka

Software Engineer at Mercari, Inc.

Tokyo, Japan
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Summary

👤
Senior
🎓
Top School
Masato Naka is a Site Reliability Engineer based in Tokyo with 10 years of software experience, focused on operating and improving large-scale microservices through SLIs/SLOs, on-call practices, and performance tuning. He transitioned from backend and infrastructure roles—leading cloud migrations, Kubernetes adoption, and CI/CD rollouts—to SRE work, bringing a full-stack operational mindset to reliability engineering. Earlier, he spent two years at ByteDance as an Algorithm Engineer improving personalized recommendation models and has contributed to the popular online-ml/river project by enhancing Factorization Machines and refactoring model internals. Multilingual in Japanese, English, and Chinese (with basic Portuguese and Korean), he leverages language skills to coordinate across global teams and accelerate cross-border projects.
code10 years of coding experience
job6 years of employment as a software developer
bookUniversity of Tokyo
bookMaster’s Degree, Master’s Degree at Jilin University
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Github Skills (12)

incremental-learning10
factoring10
machine-learning10
factors10
2factor10
python10
factorization10
numpy10
elearning10
data-science9
online-machine-learning8
streaming-data8

Programming languages (15)

MDXJavaGoMustacheHTMLJupyter NotebookHCLTypeScript

Github contributions (5)

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online-ml/river

Nov 2021 - Dec 2021

🌊 Online machine learning in Python
Role in this project:
userML Engineer
Contributions:4 reviews, 5 commits, 5 PRs in 14 days
Contributions summary:Masato primarily contributed to the `river` repository by implementing and enhancing machine learning models, particularly in the area of Factorization Machines. Their work included adding a "debug_one" method to several FMRegressor variations for detailed output analysis, simplifying calculations, and refactoring code for more efficient interaction computations. The user's contributions also involved fixing typos and improving documentation within the recommender systems example.
online-machine-learningpythonincremental-learningmachine-learningonline-learning
Contributions:14 PRs, 13 pushes, 8 branches in 2 years 10 months
golangkubernetes-operatorkubernetes
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