Akihiro Nitta
Software Engineer at Kumo
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAkihiro Nitta is a software engineer with nine years of experience combining deep learning research and production ML engineering, currently based in Mountain View and open to relocating anywhere. He has 2.5 years focused on DL/ML research and four years teaching CS/ML/DL at university, with a master's in computer science and a research background in cluster analysis and computer vision. As a core maintainer of PyTorch Lightning—one of the most popular ML frameworks—he contributes to documentation, API robustness, and developer workflows across projects that collectively serve thousands of users and contributors. His open-source footprint spans Optuna, PyTorch Geometric, TorchMetrics and Lightning Flash/Bolts, where he improves code quality, error handling, and reproducible examples for diverse tasks from image classification to graph neural networks. Known for meticulous documentation and type-safety improvements, he bridges research-grade models and reliable developer tooling to help teams ship scalable ML faster.
9 years of coding experience
4 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Kwansei Gakuin University
Japanese, English
Stackoverflow
26reputation
54reached
2answers
0questions
Github Skills (31)
pytorch10
python10
gnn10
graph10
machine-learning10
pytorch-lightning10
autoencoder10
typehinting10
deep-learning10
type-checking10
graph-neural-network10
graph-convolutional-networks10
metric10
documentation10
classification9
Programming languages (10)
Github contributions (5)
Lightning-Universe/lightning-bolts
Sep 2020 - Nov 2022
Toolbox of models, callbacks, and datasets for AI/ML researchers.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 346 reviews, 87 commits in 2 years 2 months
Contributions summary:Akihiro primarily contributed to improving the codebase's documentation and examples related to autoencoders, including fixing docstrings and updating example code for loading pretrained models. Furthermore, the user addressed exceptions, using explicit exception chaining to improve error handling across various modules, including callbacks and models. Their work encompassed multiple areas of the project, from improving the clarity of documentation to ensuring the proper loading and function of pre-trained autoencoder models, ultimately enhancing the usability and maintainability of the codebase.
pytorchsupervised-learningfairness-mlai-mlcallbacks
Lightning-AI/pytorch-lightning
Aug 2020 - Jan 2023
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Role in this project:ML Engineer
ML EngineerContributions:1670 reviews, 264 commits, 357 PRs in 2 years 5 months
Contributions summary:Akihiro primarily contributed to the documentation, error handling, and API improvements within the PyTorch Lightning library. Their work included updating and correcting documentation related to new projects, and adding better exception handling with `raise .. from ..` for improved debugging. They also updated the code to use `Optional` for arguments set to `None` by default.
pythonheadachespytorch-modelsdata-sciencehandling
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