Dexterous Grasping RL Engineer - Real Hardware

Sangha Partners; Houston (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Sangha Partners seeks a Reinforcement Learning Engineer to advance dexterous grasping on real hardware with high-DOF robotic hands. You will study recent RL advances, design experiments, and implement sim-to-real pipelines for robust manipulation in MuJoCo and Isaac Sim.

The role emphasizes hands-on policy training, reward shaping, and evaluation on physical robots, with collaboration across software teams to deploy end-to-end grasping systems.

Qualifications

• BS/MS/PhD in Robotics, CS, ML or related field.
• 2+ years of RL for grasping with reward design, exploration, and policy training.
• Experience deploying RL policies on real robotic hands.
• Ability to read, understand, and implement recent robotics/ML research.
• Experience with sim-to-real transfer: domain randomization or physics tuning.
• Proficiency in Python and DL frameworks (PyTorch, JAX) and RL libraries (rsl_rl, skrl).
• Experience preparing meshes and collision geometries for RL environments (MuJoCo/Isaac Sim).

Responsibilities

• Train and iterate RL policies for grasping tasks including functional grasping and in-hand manipulation.
• Implement sim-to-real transfer pipelines bridging simulation and hardware.
• Design rewards, exploration strategies, and curricula for grasping environments.
• Run experiments on real robots and in simulation to evaluate policy behavior.
• Stay current with research in learning-based grasping and adapt ideas to our platform.
• Collaborate with software team to deploy end-to-end grasping systems.
• Benchmark policies across object diversity and real-world uncertainty.
• Integrate tactile sensing and feedback into learned grasp policies.

Skills

Reinforcement learning

Python

PyTorch

JAX

MuJoCo

Isaac Sim

Domain randomization

Sim-to-real

Experimentation

Education

MS in Robotics/CS/ML

PhD preferred

BS accepted

Tools

MuJoCo

Isaac Sim

Python

rsl_rl

skrl

Job description

Sangha Partners seeks a Reinforcement Learning Engineer to advance dexterous grasping on real hardware with high-DOF robotic hands. You will study recent RL advances, design experiments, and implement sim-to-real pipelines for robust manipulation in MuJoCo and Isaac Sim.

The role emphasizes hands-on policy training, reward shaping, and evaluation on physical robots, with collaboration across software teams to deploy end-to-end grasping systems.