David Snyder - Machine Learning Researcher at Apple

David Snyder

Machine Learning Researcher at Apple

Seattle, Washington, United States

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Summary

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Senior

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Top School

David Snyder is a machine learning researcher with 12 years of experience specializing in deep learning for speaker recognition and diarization, currently advancing speaker segmentation and recognition on Siri at Apple. He earned a PhD from Johns Hopkins, where his thesis work produced x-vectors—an influential neural embedding for text-independent speaker recognition—and created the MUSAN augmentation dataset. David combines research rigor with production-focused engineering, contributing to the Kaldi toolkit’s core numerical routines and adding tests and documentation to its CUDA matrix code. His background spans internships at Amazon and ICF and long-term ties to JHU research programs, giving him a blend of industry and academic perspectives on robust speech systems. Based in Seattle, he’s equally comfortable prototyping novel embeddings and hardening low-level matrix code for real-world deployment, a mix that accelerates both innovation and stability.

13 years of coding experience

3 years of employment as a software developer

Rogue Community College

Bachelors of Science, Computer Science, Bachelors of Science, Computer Science at Portland State University

Johns Hopkins University

Portland Community College

Github Skills (6)

cuda10

c-language10

cprogramming-language10

linear-algebra10

unit-testing9

documentation8

Programming languages (3)

ShellC++CSS

Github contributions (5)

kaldi-asr/kaldi

Aug 2015 - Dec 2019

kaldi-asr/kaldi is the official location of the Kaldi project.

Role in this project:

Back-end Developer

Contributions:65 PRs, 533 comments, 20 issues in 4 years 4 months

Contributions summary:David's contributions primarily involve modifying and documenting functions within the `src/cudamatrix` directory of the Kaldi project, suggesting a focus on the underlying linear algebra and matrix operations. The commits include the addition of documentation to existing functions, adding comments to others and introducing unit tests. These changes indicate a focus on code clarity, usability, and testing of core numerical functions, implying a role in maintaining and improving the stability of the core code base. The contributions target files that are part of the mathematical foundation of the library.

asrkaldic-plus-pluscudashell

david-ryan-snyder/david-ryan-snyder.github.io

Sep 2017 - Jul 2019

Contributions:5 PRs, 17 pushes, 4 branches in 1 year 10 months

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