Robot Learning Engineer

• Develop, train, and deploy reinforcement learning algorithms for robotic motion and manipulation tasks.

• Build and optimize simulation infrastructure to support large-scale policy training for general-purpose robots.

• Collaborate with the controls team to integrate learned policies into the robot’s existing control stack.

• Define performance metrics, test learned policies, and evaluate their effectiveness in real-world scenarios.

• Bachelor’s or Master’s, PhD’s degree in computer science, AI, Mechatronics, Control Engineering – Automation, or a related field

• Proficiency in writing production-quality code using PyTorch/Tensorflow/JAX.

• Familiarity with online and offline RL algorithms, such as PPO and SAC.

• Familiarity with simulation technologies (e.g., IsaacGym, IsaacLab, Mujoco)

• Experience in tuning hyperparameters, reward engineering, and optimizing RL training processes.

• Knowledge of RL techniques, including domain randomization, curriculum learning, and reward shaping.

• Familiarity with machine learning evaluation tools like TensorBoard or Weights & Biases.

• Understanding of the latestest development and framework in RL for locomotion, manipulation and navigation