Reinforcement Learning Research Engineer at Agility Robotics
Portland, Oregon, United States
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Summary
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Senior
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Top School
Jonah Siekmann is a reinforcement learning research engineer with nine years of experience applying deep RL to real-world robotics, currently building learning-driven control at Agility Robotics in Portland. He earned an MS in Computer Science from Oregon State University and as a graduate researcher helped demonstrate novel bipedal behaviors—like skipping and vision-free stair-climbing—on full-scale robots through published work. Jonah’s background spans the full ML systems stack, from embedded firmware and compiler work at Halliburton and Cadence to recurrent architectures for sim-to-real transfer, giving him rare fluency in both low-level systems and research-grade learning algorithms. He combines hands-on engineering with reproducible research, and is motivated by pushing theoretical RL methods into robust physical systems.
9 years of coding experience
3 years of employment as a software developer
Honors Bachelor of Science (HBS), Computer Science, Honors Bachelor of Science (HBS), Computer Science at Oregon State University Honors College
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Oregon State University
Recurrent continuous reinforcement learning algorithms implemented in Pytorch.
Contributions:109 commits, 8 PRs, 97 pushes in 1 year 3 months
pytorchreinforcement-learning
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