Moto H
Software Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMoto H is a software engineer and machine learning specialist with 11 years of experience, currently at Meta after a multi-year engineering role at PyTorch. He has end-to-end ML production expertise—from data collection, annotation, augmentation and model training to deployment and operations—backed by strong foundations in statistics, probability and neural networks. His open-source contributions to high-profile projects like PyTorch (tutorials, torchaudio, text) and Keras demonstrate practical impact on audio and RNN examples, CI/CD, and numerical robustness. He’s built production speech systems (Capio) including Japanese ASR, alignment services and data normalization tools, and has hands-on experience converting models across frameworks. Comfortable across research and engineering, he combines sophisticated software design with practical ML operations to ship reliable systems at scale. A former aerospace M.Eng. from the University of Tokyo, he brings analytical rigor and cross-domain curiosity to applied ML problems.
11 years of coding experience
8 years of employment as a software developer
University of TokyoVisitor, Computer Science, Visitor, Computer Science at University of Toronto
French, English, Japanese
Github Skills (24)
pytorch10
learn-ruby-on-rails10
python10
signal-processing10
user-manual10
machine-learning10
rnn-model10
audio-processing10
circleci10
basics10
n10
cicd10
keras10
deep-learning10
neural-network10
Programming languages (11)
Github contributions (5)
pytorch/audio
Feb 2020 - Jan 2023
Data manipulation and transformation for audio signal processing, powered by PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:6 releases, 2101 reviews, 835 commits in 2 years 11 months
Contributions summary:Moto contributed to the torchaudio library, primarily focusing on implementing and improving audio feature extraction and processing functionalities. They worked on supporting various features such as adding a new option in `spectrogram`, improving the `filter_waveform` method, and adding new features to the `streamer` API. The user also addressed numerical instability issues and improved the usability of the code.
pytorchaudio-processingmanipulationpythonsignal
pytorch/text
Apr 2020 - Nov 2022
Models, data loaders and abstractions for language processing, powered by PyTorch
Role in this project:DevOps Engineer
DevOps EngineerContributions:244 reviews, 51 commits, 106 PRs in 2 years 7 months
Contributions summary:Moto primarily focused on improving the Continuous Integration and Continuous Deployment (CI/CD) pipeline for the `pytorch/text` repository. Their commits involved setting up and modifying CircleCI configuration files, including scripts for installing dependencies, running tests, and generating code coverage reports. The user also added jobs for binary builds and style checks to the CI pipeline, improving the automated build and testing process.
nlppytorchloadersdeep-learningdataset
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