AI Motion Engineer: Robotic Locomotion & Control

VinMotion; Cypress (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

VinMotion USA is seeking an AI Motion Engineer to develop AI-driven motion control systems for humanoid robots, focusing on reinforcement learning, whole-body control, optimal control, and AI-powered motion planning to enhance agility, balance, and dexterity.

The role involves implementing RL/Imitation Learning models, WBC/MPC, and real-time optimization on edge devices, with ROS, Mujoco, Gazebo, and Isaac Gym for testing. Collaborative cross-functional work is essential.

Qualifications

• Bachelor's or Master's degree in AI, Robotics, Mechatronics, Computer Science, or a related field.
• 2+ years of experience in AI development, robotics, or motion control.
• Hands-on experience implementing AI‑driven motion control for robotic systems.

Responsibilities

• Design and implement RL and Imitation Learning models for real-time motion adaptation.
• Develop and optimize WBC, MPC, and Motion Planning algorithms for dynamic locomotion.
• Apply Deep RL (PPO, SAC, DDPG) to robotic movement and trajectory optimization.
• Utilize Sim2Real techniques to transfer AI models from simulation to real-world deployment.
• Enhance collision avoidance, force control, and torque-based control using AI-powered predictive models.
• Collaborate with software, control, and hardware teams to ensure seamless AI integration into robotic platforms.

Skills

Python

C++

TensorFlow

PyTorch

Reinforcement Learning

Model Predictive Control

Whole-Body Control

Imitation Learning

Edge AI optimization

Education

Bachelor's or Master's degree in AI/Robotics/Mechatronics/CS or related field

Tools

ROS

Mujoco

Gazebo

Isaac Gym

Job description

VinMotion USA is seeking an AI Motion Engineer to develop AI-driven motion control systems for humanoid robots, focusing on reinforcement learning, whole-body control, optimal control, and AI-powered motion planning to enhance agility, balance, and dexterity.

The role involves implementing RL/Imitation Learning models, WBC/MPC, and real-time optimization on edge devices, with ROS, Mujoco, Gazebo, and Isaac Gym for testing. Collaborative cross-functional work is essential.