Andrew Ayres
Senior Research Software Engineer at Oak Ridge National Laboratory
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolAndrew Ayres is a senior research software engineer based in San Jose with over a decade of experience building production systems and leading teams across research labs and large tech companies. He blends deep technical rigor—anchored by a Ph.D. in Nuclear Physics—with practical software engineering at organizations such as Oak Ridge National Laboratory, AWS, and Amazon Robotics. His background includes performance-focused contributions to high-profile open source projects like Apache MXNet, where he fixed memory leaks and optimized inferencing code, reflecting a strong systems and profiling mindset. Andrew moves fluidly between low-level optimization, cryptography and compliance work at AWS KMS, and applied AI/robotics software, making him adept at turning research-grade algorithms into robust, scalable services. Known for translating complex scientific problems into maintainable software, he also brings an unusual mix of experimental physics instrumentation experience and large-scale cloud engineering.
10 years of coding experience
13 years of employment as a software developer
Ph.D. Nuclear Physics, Ph.D. Nuclear Physics at The University of Tennessee
Github Skills (5)
memory-management10
performance-optimization10
scala10
deep-learning9
api-design8
Programming languages (3)
Github contributions (5)
apache/mxnet
Jun 2018 - Mar 2019
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:20 commits, 18 PRs, 218 comments in 9 months
Contributions summary:Andrew primarily focused on improving the Apache MXNet library's performance and addressing memory-related issues. Their contributions include fixing a Scala inference memory leak and refactoring code to optimize memory usage within the `FeedForward.scala` file. They also worked on examples, updating them to use new APIs and adding tests for CI, with particular emphasis on testing profiling capabilities. These changes demonstrate a strong understanding of the library's internal workings and a focus on efficiency.
pythonschedulerdataflowmutationdata-science
andrewfayres/incubator-mxnet
Jun 2018 - Mar 2019
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:4 PRs, 79 pushes, 8 branches in 9 months
pythonschedulerdataflowmutationorchestration
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