MatthewĀ Wright
Applied Scientist at Amazon
Seattle, Washington, United States
Summary
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Top SchoolMatthew Wright is an applied scientist with 11 years of experience bridging machine learning, systems engineering, and computational biology, currently focused on representation learning for proteins. He spent a postdoctoral stint at Lawrence Berkeley Lab developing multimodal models that combine LLMs and GNNs, and applied vision transformers to electron microscopy while engineering high-performance PyTorch training pipelines for supercomputers. Matthew has moved between academic and startup environments (Berkeley, Windscape.ai) and brings practical production experience from Amazon and earlier data-science roles at Tesla and HERE. He publishes and maintains open-source PyTorch libraries for large sparse tensors with rigorous test coverage, and has explored novel extensions to Transformer positional encodings that substantially reduce memory scaling. Comfortable at the intersection of research and deployment, he mentors researchers and writes successful proposals, pairing a PhD in Mechanical Engineering with deep ML systems expertise. Based in Seattle, he combines deep theoretical work with hands-on optimization for large-scale scientific computing.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD Mechanical Engineering, Doctor of Philosophy - PhD Mechanical Engineering at University of California, BerkeleyBachelor of Science (BS) with High Honors in Mechanical Engineering Bachelor of Arts with Highest Honors in Government, Bachelor of Science (BS) with High Honors in Mechanical Engineering Bachelor of Arts with Highest Honors in Government at The University of Texas at Austin
Github Skills (71)
parallel10
python10
attention-mechanism10
hyperparameter-optimization10
deep-learning10
gpu10
ray10
optimization10
editor10
numpy10
llm-inference10
ai10
neural-network10
electron10
data-science10
Programming languages (7)
Github contributions (5)
mawright/ray
Jun 2019 - Dec 2019
A fast and simple framework for building and running distributed applications.
Contributions:60 pushes, 12 branches in 6 months
saga-patternraydistributed-applicationssimple-frameworkframework
mawright/trafficgraphnn
Oct 2017 - Jul 2019
Contributions:438 commits, 283 pushes, 5 branches in 1 year 8 months
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