7 months ago
Responsibilities
- Design and implement reinforcement-learning algorithms for robotics tasks.
- Develop and optimize RL training pipelines in 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.
Requirements
- Strong experience with reinforcement learning algorithms including PPO, SAC, TD3, and DDPG.
- Hands-on experience with robotics systems in simulation or with real robots.
- Proven experience applying reinforcement learning to manipulation, locomotion, or navigation tasks.
- Proficiency in Python and deep-learning frameworks including PyTorch, TensorFlow, and JAX.
- Strong understanding of robot kinematics, dynamics, and control.
- Experience with GPU-based simulation tools such as Isaac Gym, Isaac Lab, or SAPIEN.
- Experience with distributed reinforcement-learning training systems is preferred.
- Experience with sim-to-real transfer techniques is preferred.
- Publications in robotics or reinforcement-learning conferences such as CoRL, ICRA, RSS, NeurIPS, ICLR, or ICML are preferred.
Categories
ML EngineeringRobotics
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