Shotaro Sano
Software Engineer at Google DeepMind
Chiyoda, Japan
Summary
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Rockstar🎓
Top SchoolShotaro Sano is a software engineer and experienced engineering manager with 11 years building production ML and platform systems, currently at Google DeepMind. He led PLaMo, a from‑scratch 100B Japanese‑specialized LLM that achieved near‑frontier performance and scaled to thousands of trial users via an intuitive chat UI and API, integrating with ecosystems like AWS Bedrock and Qualcomm AI Hub. Previously he product‑led Optuna, helping grow the OSS hyperparameter optimization tool to widespread adoption and extending it into diverse real‑world use cases from JVM tuning to recipe optimization. His hands‑on ML expertise includes state‑of‑the‑art computer vision work (SE‑ResNet/ResNeXt in Chainer) and contributions to core tooling and test infrastructure in Python projects. He combines technical leadership, cross‑functional program management for nationally backed initiatives, and a commitment to open source and community building. Colleagues note his ability to move projects from prototype to large‑scale deployment while mentoring engineering teams.
11 years of coding experience
4 years of employment as a software developer
Master's degree, Master's degree at 京都大学
Stackoverflow
1reputation
544reached
0answers
1question
Github Skills (20)
python10
chainer10
enforcement10
deep-learning10
computer-vision10
coding-style10
model-driven9
back-end-development9
machine-learning9
modeling9
model-driven-development9
model-building9
type-checking8
typehinting8
data-storage8
Programming languages (5)
Github contributions (5)
optuna/optuna
Feb 2018 - Oct 2022
A hyperparameter optimization framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 108 reviews, 633 commits in 4 years 8 months
Contributions summary:Shotaro primarily focused on addressing and resolving coding style issues within the codebase, specifically addressing flake8 warnings across multiple Python files. The user also contributed to test-related improvements by adding a dummy test for the project. Furthermore, the user's efforts led to the implementation and integration of improved storage functionalities by adding type hinting to storage methods.
pythonoptimization-frameworkparallelhyperparameteroptimization
chainer/chainercv
Jun 2018 - Jun 2018
ChainerCV: a Library for Deep Learning in Computer Vision
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 1 PR, 1 comment in 3 days
Contributions summary:Shotaro implemented and set up the SEResNet and SE-ResNeXt architectures within the ChainerCV library. This involved defining the network structures, including the integration of squeeze-and-excitation blocks. They also added functionality for downloading pretrained models and updated the global average pooling operation, showcasing expertise in computer vision model development and Chainer framework utilization.
deep-learningpytorchcomputer-visionvision
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