Tomu Hirata
Software Engineer at Databricks
Tokyo, Japan
Summary
🤩
Rockstar🎓
Top SchoolTomu Hirata is a software engineer based in Tokyo with 8 years of experience building production-ready infrastructure, ML systems, and developer tooling. He maintains ML open-source packages at Databricks and has contributed to high-profile projects like MLflow and StanfordNLP's DSPy, focusing on integration, code quality, and maintainability. Previously he led a team at Indeed on sponsoring models and built messaging backends and cloud-native platforms, giving him a strong mix of backend, ML engineering, and product-facing experience. As the first engineer at estie he delivered full-stack solutions from frontend to ETL and cloud infra, demonstrating end-to-end ownership. He holds advanced degrees from the University of Tokyo and UCL, combining technical depth in information science with public health insights. Colleagues describe him as a pragmatic engineer who brings rigorous testing and type-focused refactors to complex ML codebases.
8 years of coding experience
5 years of employment as a software developer
Master's degree, Information Science and Technology, GPA 3.87, Master's degree, Information Science and Technology, GPA 3.87 at 東京大学University of TokyoUniversity College London
Github Skills (15)
apim10
machine-learning10
mlflow10
api10
dsym10
python10
testing10
refactor9
debug9
refactoring9
debugging9
openai8
type-checking8
mlops8
typehinting8
Programming languages (10)
Github contributions (5)
stanfordnlp/dspy
Nov 2024 - Apr 2025
DSPy: The framework for programming—not prompting—language models
Role in this project:
Software Engineer (Focus on Software & API Integration)
Software Engineer (Focus on Software & API Integration)Contributions:24 reviews, 45 PRs, 9 pushes in 4 months
Contributions summary:Tomu contributed significantly to the `stanfordnlp/dspy` repository, primarily by addressing bug fixes and making improvements to the codebase. Their commits focused on resolving import issues, fixing code style violations (F401, E701), and correcting model name errors during finetuning. Furthermore, the user worked on refactoring the `ProgramOfThought` module and enhancing the type hints of adapters, indicating a focus on code quality, maintainability, and system integration within the DSPy framework. They demonstrated an understanding of various aspects of the project architecture.
nlpbertknowledgepredictlanguage-models
mlflow/mlflow
Oct 2023 - Apr 2025
Open source platform for the machine learning lifecycle
Role in this project:Backend Developer & ML Engineer
Backend Developer & ML EngineerContributions:1 release, 566 reviews, 170 PRs in 1 year 6 months
Contributions summary:Tomu primarily contributed to the codebase by adding and modifying Ruff rules, indicating a focus on code quality and potentially automated style enforcement within the project. The user made changes to files related to machine learning model serving, and implemented improvements to the model signature and logging, and enhanced the tool functionality used by agents. The user also refactored tests, indicating a commitment to improving the project's testing infrastructure, demonstrating skills in Python and ML engineering.
pythonlifecyclemlmachine-learningincremental-learning
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