Reinforcement Learning Engineer

Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona's founding team has a decades-long history in humanoid robotics, bionics, and product development delivering robust hardware that has touched the stars, worked miles below the surface of the ocean, roamed Disney Parks, and has even been featured on a US postage stamp. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims.

Role Overview:

We are looking for a Reinforcement Learning Engineer to join our Manipulation team, focused on dexterous grasping. Our goal is to ship capable, reliable grasping policies on real hardware with high-DOF robotic hands. We are looking for someone who can follow recent advances in reinforcement learning and related learning-based methods, judge what is practically useful, and adapt those ideas on our platform. If you are earlier in your career but exceptional, we want to hear from you; equally, a more experienced candidate who brings deep RL expertise will thrive here.

Your Role:

• Train and iterate on reinforcement learning policies for complex grasping tasks including functional grasping, tool use, in-hand manipulation, and environment interaction.

• Implement and refine sim-to-real transfer pipelines to bridge the gap between simulation and physical robotic hand performance.

• Develop reward functions, curriculum strategies, and training environments in MuJoCo and Isaac Lab.

• Run experiments on real robots alongside simulation, evaluating and debugging policy behavior on hardware.

• Monitor, evaluate, and adapt state-of-the-art research in learning-based grasping to deploy on our humanoid platform.

• Collaborate with the rest of the software team to deploy end-to-end grasping systems.

• Benchmark and evaluate grasp policies across object diversity, clutter scenes, and real-world uncertainties.

• Integrate tactile sensing and feedback into grasp policies for robust, force-aware manipulation.

We're Looking For:

• BS, MS, or PhD in Robotics, Computer Science, Machine Learning, or a related field.

• 2+ years of hands-on experience in reinforcement learning for robotic manipulation; exceptional recent graduates from relevant research labs will be considered.