Jonathon Schwartz
Member Of Technical Staff at Reflection AI
New York City Metropolitan Area United States
Summary
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Senior🎓
Top SchoolJonathon Schwartz is a Member of Technical Staff building open intelligence at Reflection AI with nine years of experience bridging academic research and production-grade systems. He holds a PhD in multi-agent reinforcement learning and planning from ANU and has published research on scalable planning and planning-under-uncertainty for tasks like network penetration testing. Jonathon has built high-fidelity research environments (CybORG) and scalable AWS infrastructure to support multi-user autonomous cyber operations, and recently focused on developing reasoning agents at Imbue. His work blends deep algorithmic expertise with practical engineering—shipping cloud-hosted simulation platforms and agent frameworks. Active on GitHub and Google Scholar, he contributes code and papers that advance better reasoning agents rather than just model benchmarks. Based in the New York City area, he brings a rare combination of multi-agent RL research, systems engineering, and applied security experience.
9 years of coding experience
3 years of employment as a software developer
Australian National UniversityBachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at The University of Queensland
English, French
Github Skills (25)
testing10
reinforcement-learning10
ai10
pentesting10
cpp10
parallel9
algorithms9
python9
large-language-models9
gymnasium9
optimization9
ray8
deep-learning8
hyperparameter-optimization8
tensorflow8
Programming languages (4)
Github contributions (5)
Jjschwartz/NetworkAttackSimulator
Jul 2018 - Jul 2022
An environment for testing AI pentesting agents against a simulated network.
Contributions:12 releases, 199 commits, 22 PRs in 4 years 1 month
aipentestingtesting
Jjschwartz/Jjschwartz.github.io
Nov 2018 - Dec 2021
A collection of papers and other publications that I have written or been involved in
Contributions:2 PRs, 11 pushes, 2 branches in 3 years
deep-learningmachine-learningpublicationsdata-science
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