Policy Design & Training: You design and train the learning-based policies that map multimodal sensing, from vision and language down to raw tactile and IMU data, into smooth, precise, and safe hardware actions.
Multi-Contact Manipulation: You sit at the intersection of imitation learning, reinforcement learning, and physical deployment. Your problem is the hard one: multi-contact manipulation on real, underactuated, tactile-rich hardware.
Dexterous Hand Control: You teach NEURA's hands to manipulate the world with human-like dexterity
Collaboration & Execution: You work closely with ML, robotics, and software teams to deliver trained policies that work on the hands.
Master's or PhD in Robotics, Computer Science, Machine Learning, or a related field with a strong focus on robotic manipulation or reinforcement learning
Hands-on experience training manipulation policies with imitation learning or deep RL on physical robot arms or dexterous hands
Deep familiarity with GPU-accelerated simulation (Isaac Lab and Isaac Sim, or MuJoCo), including building custom environments and assets
Strong PyTorch, with experience using robot-learning libraries such as Stable-Baselines3, Ray RLlib, or LeRobot
Solid foundations in kinematics, dynamics, spatial transforms, and closed-loop control
Proficient Python and C++, clean reproducible code, Git, and Docker
Nice to have:
Experience with teleoperation hardware such as VR controllers, data gloves, or vision-based hand tracking
Experience training or fine-tuning VLA or diffusion-based architectures
Experience with tendon-driven or highly underactuated mechanical systems