We seek a Robotics DevTech Engineer to join our NVIDIA Omniverse team in China. This role serves as the local technical bridge linking the robotics community across China and the global NVIDIA platform team. Function as the primary contact for robotics partners in China, translating technical needs into actionable insights for engineering teams. Your mission: reduce technical friction, build team expertise in Isaac workflows, enable rapid partner iteration, and build the product roadmap with market intelligence from China.

What You Will Be Doing:

• Local Ecosystem Triage & Support: First technical point of contact for Chinese robotics companies and research institutions. Conduct technical assessments, resolve integration and debugging issues locally in Mandarin, and build relationships with key partners.

• Robotics Workflow Mastery: Develop deep expertise across Isaac Sim, Isaac Lab, SimReady, and emerging tools. Build internal playbooks for common use cases (humanoid sim-to-real, AMR navigation, manipulation with RL) that your team can reference.

• Link Between Partners & Engineering: Distill partner requirements into clear technical briefs for global OV engineering teams. Track blockers across partners, raise critical issues and feature requests, and enable rapid feedback loops when new capabilities ship.

• Regional Insight Generation: Identify emerging trends in China's robotics industry, preferred tech stacks, and localized challenges. Share market intelligence with NVIDIA leadership to inform product strategy.

What We Need To See:

• MS or PhD in Computer Science, Robotics, Mechanical Engineering, or related field

• Minimum 5 years in robotics engineering, simulation, or AI/ML development

• Real Robot Experience: Proven hands-on work with robotic systems (manipulators, humanoids, drones)

• Proficient Mandarin Chinese & Professional English

Core Technical Skills

• Deep hands-on experience with: IsaacSim/Lab or MuJoCo, or Gazebo

• Proficiency in Python and C++ for robotics algorithms and performance optimization

• ROS/ROS2 and deep learning frameworks (PyTorch)

AI & Control Fundamentals

• Reinforcement learning concepts and imitation learning

• Robot control, kinematics, dynamics, and trajectory optimization

• Synthetic data generation and domain randomization for sim-to-real transfer

Ways To Stand Out from the Crowd:

• Multi-domain robotics experience across different robot morphologies and applications

• Deep understanding of physics engine tradeoffs (PhysX vs MuJoCo), contact tuning, USD/Omniverse experience building digital twins for robotics simulation

• Published research at top robotics conferences (ICRA, IROS, CoRL, ICLR)

• Familiarity with the robotics sector in China

• Active open-source contributions (ROS, Isaac Lab) with mentoring experience