Reinforcement Learning Engineer, Grasping
Persona AI Incabout 3 hours ago
Houston, TX, USAEntry Level / Mid Level
H1B Sponsor
Responsibilities
- Train and iterate on reinforcement learning policies for complex grasping tasks.
- Implement and refine sim-to-real transfer pipelines for robotic hand performance.
- Develop reward functions and training environments in MuJoCo and Isaac Lab.
- Run experiments on real robots and evaluate policy behavior on hardware.
- Monitor and adapt state-of-the-art research in learning-based grasping.
- Collaborate with the software team to deploy end-to-end grasping systems.
- Benchmark and evaluate grasp policies across object diversity and real-world uncertainties.
- Integrate tactile sensing into grasp policies for robust manipulation.
Requirements
- 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.
- Ability to read and implement ideas from recent robotics and machine learning research.
- Hands-on experience training RL agents for robotic manipulation tasks.
- Experience with sim-to-real transfer and real-world policy validation on hardware.
- Proficiency in Python and deep learning frameworks like PyTorch or JAX.
- Experience preparing meshes and collision geometries for RL environments.
Benefits
- Competitive compensation and performance-based bonus.
- 99% employer covered medical benefits.
- Early-stage equity and competitive PTO.
- Company-wide paid winter break from December 24th to January 2nd.
- Access to advanced tools and hardware labs.