Hayata Yamamoto
創設者 at Tied株式会社
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolHayata Yamamoto is a Tokyo-based founder and engineering leader with nine years of experience building ML-driven products and leading teams from data science to CTO responsibilities. He co-founded Tied株式会社 after serving as CTO/CHRO at Todoker, where he guided product and engineering efforts and shipped production machine learning systems like PROGOS. Hands-on across Python, Go, SQL and cloud infrastructure, he has a track record of moving NLP and acoustic models from PoC to product while also owning testing and integration work. An active open-source contributor, he extended Optuna’s distribution API—demonstrating attention to robust hyperparameter tooling used widely in ML research. His background in economics and early sales roles gives him a pragmatic, cross-functional perspective on turning data into business value.
8 years of coding experience
7 years of employment as a software developer
Bachelor of Arts - BA, Economics, Bachelor of Arts - BA, Economics at 千葉大学
Stackoverflow
71reputation
14kreached
0answers
3questions
Github Skills (10)
hyperparameter-optimization10
machine-learning10
python10
distributions10
testing10
centos6
evaluation6
optimization6
scikit-learn6
classification6
Programming languages (12)
Github contributions (5)
optuna/optuna
Feb 2020 - Mar 2020
A hyperparameter optimization framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:31 commits, 1 PR, 26 comments in 1 month
Contributions summary:Hayata primarily contributed to the `optuna/optuna` repository by adding a new parameter 'q' to the `IntUniformDistribution` class, reflecting the core focus of the repository. The contributions include code changes in several tests to support the added feature, especially in test_distributions.py and test_trial.py files, indicating that the developer was familiar with the structure and testing practices in this project. Furthermore, changes were made in other integration tests as well.
pythonoptimization-frameworkparallelhyperparameteroptimization
tied-inc/eval-track
Dec 2024 - Mar 2025
LLM-ML-Observability Toolkits and Serivces
Contributions:28 reviews, 65 PRs, 60 pushes in 2 months
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