Harutaka Kawamura
Senior Software Engineer at Databricks
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolHarutaka Kawamura is a Senior Software Engineer based in Tokyo with 10 years of experience building and productionizing ML systems, back-end services, and developer tools. Currently at Databricks, he blends ML lifecycle expertise (notably MLflow) with strong backend and type-safe Python contributions across major OSS projects like pip, scikit-learn, Keras and Optuna. He’s comfortable across the stack—from optimizing hyperparameter visualization and LLM integration to writing linting rules, compiler fixes in Rust-based projects, and test automation for plotting libraries. His background in materials science informs a pragmatic, analytical approach to problem solving, and he has shipped practical tooling such as Chrome extensions and Databricks-focused productivity improvements. Active in high-profile open source, Harutaka often contributes documentation and reproducible examples that make complex tools more accessible to engineers.
10 years of coding experience
6 years of employment as a software developer
Master's degree, Materials Science, Master's degree, Materials Science at Toyota Technological Institute
Japanese, English
Stackoverflow
119reputation
15kreached
0answers
7questions
Github Skills (62)
documentation10
data-analysis10
unit-testing10
parserator10
git10
static-code-analysis10
apache-spark10
notebook10
parser10
pytest10
python10
data-science10
abstract-syntax-tree10
testing10
scikit10
Programming languages (16)
Github contributions (5)
mlflow/mlflow
Oct 2019 - Jan 2023
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:18 releases, 14956 reviews, 1080 commits in 3 years 3 months
Contributions summary:Harutaka's commits primarily focused on enhancing the MLflow platform, demonstrating expertise in machine learning and model management. Their contributions included removing virtual environment errors, migrating test databricks model artifact repositories to pytest, and optimizing test performance. They implemented fixes to facilitate LLM integration and improved the handling of model dependencies.
pythonlifecyclemlmachine-learningincremental-learning
optuna/optuna
Nov 2019 - Jun 2022
A hyperparameter optimization framework
Role in this project:ML Engineer
ML EngineerContributions:77 reviews, 272 commits, 72 PRs in 2 years 6 months
Contributions summary:Harutaka primarily contributed by adding and modifying a quickstart example notebook (`examples/quickstart.ipynb`) and a plotting example notebook (`examples/visualization/plot_study.ipynb`). This included installing necessary libraries like Optuna and Plotly, defining an objective function using scikit-learn, and running hyperparameter optimization experiments. The user's work demonstrates a focus on demonstrating and visualizing the functionality of the Optuna framework for hyperparameter optimization.
pythonoptimization-frameworkparallelhyperparameteroptimization
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