AI Manipulation Engineer

Role Summary:
Engineers in this role will design, implement, and optimize advanced AI algorithms that enable humanoid robots to perceive, plan, and execute manipulation tasks using cutting-edge techniques like reinforcement learning (RL), diffusion/flow models, and vision-language-action (VLA) models. They’ll bridge research and real-world execution, focusing on robust, scalable manipulation behaviors.

Key Responsibilities:

• Develop manipulation algorithms for high-DOF humanoid hands (grasping, pick/place, bimanual tasks).

• Build and train RL-based policies and generative models (diffusion/flow) for robust manipulation.

• Integrate VLA models into perception-to-action pipelines for instruction-driven manipulation.

• Create and maintain sim-to-real pipelines using simulation tools (MuJoCo, IsaacSim/Lab, etc.).

• Collaborate with control, perception, and hardware teams to deploy algorithms on real robots.

• Evaluate performance metrics, conduct experiments, and iterate based on real-world tests.

Qualifications:

• Bachelor’s / Master’s / PhD in Robotics, Computer Science, Mechatronics, or related field.

• 2+ years of experience in AI/robotics manipulation.

• Strong programming skills in Python and/or C++; experience with ML frameworks (PyTorch, TensorFlow).

• Experience with RL training, reward engineering, and simulation environments.

• Knowledge of ROS/ROS2 and real-robot deployment.

• Solid understanding of motion planning, kinematics, and perception systems.

Preferred:

• Prior work with VLA or foundation models in robotics.

• Publications in relevant AI/robotics conferences.

• Familiarity with real-world robotics testing and hardware integration.