Reinforcement learning engineer

Role Overview

We're seeking Reinforcement Learning experts to develop and deploy cutting-edge RL algorithms that enhance our robots' capabilities.

Responsibilities

• Design and implement reinforcement learning algorithms for various robotics tasks

• Develop and optimize RL training pipelines in both simulation and real-world environments

• Collaborate with robotics engineers to integrate RL models into production systems

• Conduct experiments to evaluate and improve algorithm performance

• Scale training infrastructure for efficient learning across multiple robots

Required Qualifications

• Strong experience with reinforcement learning (PPO, SAC, TD3, DDPG, etc.)

• Hands-on experience with robotics systems (simulation or real robots)

• Proven track record applying RL to manipulation, locomotion, or navigation tasks

• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX)

• Strong understanding of robot kinematics, dynamics, and control

• Experience with GPU-based simulation such as Isaac Gym, Isaac Lab, SAPIEN, etc.

Preferred Qualifications

• Experience with distributed RL training systems

• Experience with sim-to-real transfer techniques

• Publications in robotics or RL conferences (CoRL, ICRA, RSS, NeurIPS, ICLR, ICML, etc.)