Nathan Tokala
ML AI Senior Software Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolNathan Tokala is an ML/AI senior software engineer based in New York with nine years of experience building data-centric platforms, full-stack systems, and production ML pipelines. At Meta he led teams designing privacy-focused data collection and labeling infrastructure that powers next-generation assistant models and improvements in NLU and ASR. His background spans building high-throughput annotation tooling at Lodestone, cost-saving data transformations in finance, and scalable web frameworks for large media clients. An active open-source contributor in speech and audio, he implemented the SVoice speech separation model in the widely used ESPnet toolkit and added audio transforms to PyTorch/audio, blending research-grade algorithms with production-quality testing. He combines a pragmatic engineering approach with research interests in speech, audio, and language, often delivering measurable efficiency and cost improvements across teams.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Lehigh University
Github Skills (11)
pytorch10
machine-learning10
speech-recognition10
audio-processing10
deep-learning10
sound-processing10
python10
signal-processing10
digital-signal-processing10
unit-test9
deeplearning-ai8
Programming languages (5)
Github contributions (5)
pytorch/audio
Jul 2021 - Jan 2023
Data manipulation and transformation for audio signal processing, powered by PyTorch
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 758 reviews, 159 commits in 1 year 6 months
Contributions summary:Nathan primarily contributed to the audio signal processing functionalities within the PyTorch audio library, adding and integrating new features related to pitch shifting and other audio transformations. Their work involved modifying existing functional modules and transforms, integrating new functionalities such as the `PitchShift` transform and `InverseSpectrogram`. Furthermore, the user also added new tests to ensure consistency and performance and fixed examples in transforms. The user also added support to various models that include features to build and modify the current functionalities.
audiopytorchsignal-processingpythonio
espnet/espnet
Mar 2022 - Apr 2022
End-to-End Speech Processing Toolkit
Role in this project:ML Engineer
ML EngineerContributions:5 reviews, 7 commits, 2 PRs in 1 month
Contributions summary:Nathan contributed to the development of the SVoice speech separation model, a core component of the End-to-End Speech Processing Toolkit. Their work involved implementing the SVoice model, including encoder, decoder, and DPMulCat blocks, and integrating it within the existing framework. Furthermore, the user addressed code quality issues and added unit tests to ensure the model's correctness and robustness.
speech-processingdeep-learningend-to-endchainerpytorch
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