Reinforcement Learning Engineer, Grasping

Overview:

We are looking for a Reinforcement Learning Engineer to join our client's Manipulation team, focused on dexterous grasping. Their 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 their platform.

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.
• Design reward functions and exploration strategies specific to grasp acquisition, along with 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 specifically applied to grasping- reward design, exploration strategy, and policy training built around picking up and manipulating objects (not general manipulation, locomotion, or whole-body control)
• Experience deploying RL-trained policies on physical robotic hands- real hardware validation, not simulation-only work.
• Demonstrated ability to read, understand, and implement ideas from recent robotics and machine learning research.
• 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 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.