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.
- Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
- Hands-on experience training RL agents for robotic manipulation tasks, including reward shaping and policy evaluation.
- Experience with sim-to-real transfer: domain randomization, physics tuning, or real-world policy validation on hardware.
- Proficiency in Python and deep learning frameworks (PyTorch, JAX), along with RL libraries such as rsl_rl or skrl.
- Experience preparing meshes and collision geometries for RL environments in simulators such as MuJoCo and/or Isaac Sim.
Bonus Qualifications:- Experience deploying RL-trained policies on physical robotic hands.
- Experience with tactile sensors and integrating tactile feedback into learned grasp policies.
- Experience with contact-rich manipulation and force/torque estimation.
- Familiarity with other learning-based approaches such as behavior cloning, imitation learning, or diffusion-based policy methods.
- Publications or project work at top-tier venues (CoRL, RSS, ICRA) on grasping or dexterous manipulation.
- Experience in a humanoid robot startup environment.
Why Join Persona AI?- We offer competitive compensation, a performance-based bonus, 99% employer covered medical benefits, early-stage equity, competitive PTO, and a company-wide paid winter break between December 24th and January 2nd.
- You'll shape technology that's redefining the possibilities of robotics and human interaction.
- Work alongside passionate teammates who value creativity, collaboration, and continuous learning.
- Enjoy full access to advanced tools, hardware labs, and the freedom to push the boundaries of what robots can do.
