Persona AI
Reinforcement Learning Engineer, Grasping
HoustonfulltimemidAdded 1 month ago
About this role
Persona AI seeks a Reinforcement Learning Engineer to develop dexterous grasping policies for humanoid robots. You'll train RL agents in simulation and deploy them on real hardware, focusing on sim-to-real transfer and complex manipulation tasks.
What you'll do
- Train and refine RL policies for grasping, tool use, and in-hand manipulation tasks
- Develop sim-to-real transfer pipelines using MuJoCo and Isaac Lab simulators
- Design reward functions and curriculum strategies for complex grasping behaviors
- Test and debug policies on physical robotic hands in real-world conditions
- Integrate tactile sensing and feedback into grasp control systems
- Evaluate and benchmark grasping performance across diverse objects and scenarios
What they're looking for
- Reinforcement learning (RL) for robotic manipulation
- Python and deep learning frameworks (PyTorch, JAX)
- RL libraries (rsl_rl, skrl) and simulation environments
- Sim-to-real transfer techniques and domain randomization
- MuJoCo and Isaac Sim experience with mesh/collision preparation
- Hardware testing and real-world policy validation
- Reward shaping and policy evaluation methodologies
- Contact-rich manipulation and tactile sensor integration
Benefits
- Competitive compensation with performance-based bonus
- 99% employer-covered medical benefits
- Early-stage equity
- Competitive paid time off
- Paid company winter break (December 24 - January 2)
- Access to advanced hardware labs and cutting-edge robotics tools
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Persona AI
Persona AI builds humanoid robots and manufactures them at scale. The company is hiring for specialized engineering roles including quality assurance, reinforcement learning, process optimization, robotics software development, and welding to advance their robotics platform.
- Website
- persona.ai
Likely interview questions
- Walk us through a reinforcement learning project you've implemented for robotic manipulation. How did you design the reward function, and what challenges did you encounter?
- Describe your experience with sim-to-real transfer. What domain randomization or physics tuning techniques have you used, and how did you validate policies on real hardware?