Explore jobs tagged policy-optimization in our jobs listing to find openings that demand expertise in policy optimization, reinforcement learning, policy gradients (PPO, TRPO), actor-critic methods, reward modeling, and sample-efficient optimization for continuous and discrete control. This filtered list surfaces roles across industries—robotics, autonomous systems, finance, and recommendation engines—that require practical RL experience, MLOps for model deployment, simulation-driven training, and proficiency with TensorFlow, PyTorch, or RL libraries. Use the filtering UI to narrow by experience level, tech stack, remote/on-site, and publication or patent history; review detailed job descriptions, application links, and hiring criteria, then apply or save listings to connect with employers actively recruiting policy-optimization talent.
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