We aim to advance the mobility and autonomy of mobile robots to new heights by leveraging simulation-based methods like Reinforcement Learning, augmenting them with model information.
Reinforcement Learning Research Scientists will have proven hands-on research or industry experience focusing on one or more of these key areas: Learning-based locomotion, loco-manipulation, or ultra-mobile systems. Having practical hardware experience is essential for this role. If you are passionate about developing technology for robots and using it to advance their capabilities and usefulness, this team will be a great fit for you!
What you will need
MS / PhD or equivalent industry experience in robotics, computer science, or related fields4+ years of experience in research and developmentAbility to demonstrate technical proficiency in Reinforcement Learning, Control, Robotics, or Imitation LearningExperience with sim-to-real for robotic hardware specifically legged robots or highly dynamic mobile vehicles.Experience in working with perception-based controlAdvanced programming skills in Python or C++Expertise with deep learning frameworks such as PyTorch and robotic simulationProven track record in top-tier conferences and journals in Machine Learning, Robotics, Control, or related fields
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Bonus
Knowledge of Model Predictive ControlKnowledge in Robot navigation and perceptionExperience in combining model-based and data-driven approachesExperience in working with Isaac-Gym, Isaac-Sim, or OrbitExperience in working with ROS or ROS2Experience with Docker, cloud computing, or similar applicationsExperience with parallel programming (e.g., CUDA)
These attributes are great to have but not required. Candidates who lack these should not be discouraged from applying.
