AI Manipulation Engineer (human)

Your mission & challenges

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.

What we can look forward to

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