Garret Catron
Software Engineer at Meta
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolGarret Catron is a software engineer with a decade of experience building high-performance C++ and Python systems, currently contributing at Meta in the San Francisco Bay Area. He combines a strong academic foundation—a Master's in Computer Science from USC—with practical expertise in computer vision (OpenCV), robotics middleware (ROS, DDS), web backends (Django, REST) and databases (SQL). At scale, he has improved compiler backend stability and memory management for the widely used PyTorch/Glow neural network compiler, demonstrating attention to low-level performance and testability. His earlier work spans defense and industry roles, giving him a disciplined systems mindset and a track record of shipping reliable runtime and allocation improvements. Colleagues rely on him for pragmatic, test-driven solutions that bridge research-grade tooling and production demands.
10 years of coding experience
10 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at University of Southern CaliforniaBachelor of Science (BS) Electrical and Computer Engineering, Bachelor of Science (BS) Electrical and Computer Engineering at Andrews University
Github Skills (6)
unit-testing10
c-language10
cprogramming-language10
back-end-development10
neural-network9
caffe9
Programming languages (2)
Github contributions (5)
pytorch/glow
Oct 2018 - Aug 2021
Compiler for Neural Network hardware accelerators
Role in this project:
Backend Developer
Backend DeveloperContributions:194 commits, 196 PRs, 38 pushes in 2 years 11 months
Contributions summary:Garret primarily contributed to the C++ backend of the Glow compiler for neural network hardware accelerators. Their work included adding unit tests for Caffe2 importer functionality, improving CPU backend allocation and code generation for placeholder migration, and updating tests to support new function signatures. Additionally, the user made contributions to core runtime components, including refactoring the Provisioner to reduce complexity and improve memory management. The user's focus was on enhancing the functionality, stability, and efficiency of the Glow compiler.
hardware-acceleratorscompilerneural-networkacceleratorshardware
gcatron/pytorch
Sep 2020 - Apr 2021
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:48 pushes, 9 branches in 6 months
pythongpu-accelerationdeep-learninggpuacceleration
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