Taylor Robie
Senior Software Engineer at Meta
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top School
Top expert inArtificial Intelligence and Machine Learning Technologies
Taylor Robie is a Senior Software Engineer with a decade of experience building and optimizing ML infrastructure, currently contributing to PyTorch at Meta from Mountain View. He has a strong track record across major ML projects—earlier roles include TensorFlow work at Google and PyTorch contributions at Facebook and Lightning AI—bridging research-quality tooling and production performance. Taylor specializes in backend performance engineering, notably improving the PyTorch profiler and memory profiler by refactoring tracers, handling tensor IDs, and unifying event types for more accurate and efficient profiling. His background in chemical engineering brings a quantitative, experimental mindset to system optimization and performance debugging. Comfortable in both research and product environments, he focuses on making complex GPU-accelerated ML tooling robust, maintainable, and fast.
10 years of coding experience
9 years of employment as a software developer
Master of Science (M.S.) Chemical Engineering, Master of Science (M.S.) Chemical Engineering at University of CincinnatiMaster of Chemical Engineering Chemical Engineering, Master of Chemical Engineering Chemical Engineering at University of Delaware
English
Github Skills (19)
pytorch10
c-language10
performance-analytics10
performance-monitor10
python10
performance-measurement10
performance-analysis10
profiling10
performance-tuning10
memory-profiling10
tensor10
performance-monitoring10
cprogramming-language10
data-structure9
debug9
Programming languages (6)
Github contributions (5)
pytorch/pytorch
Mar 2020 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:466 reviews, 1472 commits, 315 PRs in 2 years 9 months
Contributions summary:Taylor made several contributions focused on optimizing the PyTorch profiler. They worked on building call trees, refactoring the cache structure for the python tracer to be more modular and efficient, and moving the python tracing to the unified event type for better integration. They also contributed to improving the memory profiler by handling tensor IDs and ensuring accurate end times for events. Additionally, the user made various code changes to improve the performance of the memory profiler and ensured that the code was robust and maintainable.
pythongpu-accelerationdeep-learninggpunumpy
robieta/pytorch
Dec 2020 - Nov 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:171 pushes, 37 branches in 1 year 11 months
pythongpu-accelerationdeep-learninggpuacceleration
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