Sato Motoki
防衛部門長 at Sakana AI
Nara, Nara Prefecture, Japan
Summary
👤
Senior🎓
Top SchoolSato Motoki is an experienced AI and software engineering leader with 11 years focused on NLP, speech recognition, deep learning, and anomaly detection, currently heading the defense division at Sakana AI after roles as Project Manager and prior leadership at Preferred Networks. He combines hands-on research from NAIST’s Matsumoto Lab with production-grade engineering—contributing to prominent open-source ML projects like Chainer and CuPy by implementing and rigorously testing core numerical functions for GPU workloads. Known for bridging research and delivery, he has progressed from part-time software engineer to general manager while shipping robust models and infrastructure for dialog, QA, NER, and Japanese-specific segmentation. Based in Nara, he brings a pragmatic mix of academic depth and systems-level QA discipline that helps move novel NLP and speech methods into reliable products.
11 years of coding experience
9 years of employment as a software developer
Master's degree, Computer Science, 1, Master's degree, Computer Science, 1 at Nara Institute of Science and TechnologyBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Nagoya Institute of Technology 名古屋工業大学
Github Skills (14)
testing10
machine-learning10
python10
cupy10
numpy10
chainer10
tensorflow9
deep-learning9
tensor9
unit-testing8
cuda8
gpu7
deeplearning-ai7
neural-network6
Programming languages (3)
Github contributions (5)
chainer/chainer
Oct 2016 - Aug 2018
A flexible framework of neural networks for deep learning
Role in this project:
ML Engineer
ML EngineerContributions:110 commits, 22 PRs, 79 comments in 1 year 10 months
Contributions summary:Sato primarily contributed to the implementation and testing of a `squared_difference` function within the `chainer` deep learning framework. Their work involved adding the core function, modifying the backward function for gradient calculations, and creating comprehensive test cases to ensure its correctness, including GPU testing. These contributions demonstrate a focus on extending the framework's functionality and ensuring its reliability through rigorous testing.
cudapythonmxnetcaffe2flexible-framework
cupy/cupy
Sep 2017 - Apr 2019
NumPy & SciPy for GPU
Role in this project:Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:28 commits, 2 PRs, 6 comments in 1 year 7 months
Contributions summary:Sato contributed to the CuPy library by implementing and testing a squared difference function within the chainer/functions/math directory. Their work included defining the function, modifying its backward implementation, and adding comprehensive test cases. The user also integrated the new function into the chainer/functions/\_\_init\_\_.py module and updated the documentation to reflect the change.
cudapythoncusolvergpunumpy
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