Haresh Karnan is an ML researcher and RL post-training engineer at Amazon AGI with 12 years of experience building large-scale foundation models and robot learning systems. He holds a PhD from UT Austin where he published first-author work on sim-to-real transfer and grounded action transformation, and he has hands-on autonomy experience from training VLMs and leading RL post-training at Zoox. His background blends deep reinforcement learning, self-supervised representation learning, and computer vision for real-world robotics and autonomy applications. Past roles include multiple applied scientist internships at Amazon focused on Scout localization and a robotics-focused MS and BTech, giving him strong systems and hardware-aware ML intuition. He’s comfortable moving ideas from simulation to physical systems—his RoboCup work and robot demos reflect a practical bent for deploying learning agents in messy environments. Based in San Francisco, he combines academic rigor with production-scale model training for autonomy and AGI safety-aligned research.
12 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at The University of Texas at Austin
Bachelor of Technology (BTech), Instrumentation and Control Engineering, Bachelor of Technology (BTech), Instrumentation and Control Engineering at National Institute of Technology, Tiruchirappalli
Master's degree, Robotics, Master's degree, Robotics at Texas A&M University
Computer Science, Computer Science at D.A.V. Gopalapuram
Contributions:2 reviews, 27 commits, 2 PRs in 8 months
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