We are looking for a hands-on Robotics Engineer with a passion for bringing AI to physical hardware. In this role, you will beat the core of our Embodied AI (Physical AI) pipeline, focusing ontransforming intelligent models into real-world robot actions. Your primary responsibilitywill be programming, control, and real-world deployment on physicalmanipulator arms (such as Franka or UR) . If you love working with realhardware, setting up cameras, and making mechanical arms move precisely andintelligently in the physical world, this is the role for you. Key Responsibilities ● Real-Arm Deployment & Control: Design, implement, and maintain ROS 2-based control systems directly deployedon physical collaborative robots (cobots), specifically Franka, UR , orsimilar robotic arms. ● Embodied AI & Policy Integration: Develop core robot manipulation and behavior components using C++ and Python .Collaborate with the AI team to deploy VLA/VLM (Vision-Language-Action) models and imitation learning policies on real hardware. ● Full-Stack Robotics Integration: Integrate perception (e.g., cameras, sensors), manipulation primitives, andcontrol modules into a unified, reliable real-world system. ● Hardware Debugging & Optimization: Debug, profile, and optimize low-level robot control loop performance, addressreal-world latency, and solve sim-to-real gaps directly on the hardware. ● Simulation Support (Secondary): Utilizesimulation tools (e.g., Isaac Sim/Gazebo) as needed for basic policy trainingor initial behavioral testing before full-scale real-world deployment. Required Skills & Qualifications ● Education: Bachelor’s degree orhigher in Robotics, Computer Science, Computer Engineering, MechanicalEngineering, or a related technical discipline. (Strong personalportfolio/projects will be highly valued). ● Hands-on Robot Experience: Strong, proventrack record of programming and controlling real robotic arms (Franka,Universal Robots, etc.) . ● Robotics Frameworks: Deep experience with ROS 2 and a solid foundation in robotics fundamentals ( kinematics,dynamics, control paths, trajectories, and sensing ). ● Programming: High proficiency in Python for real-time or near-real-time applications. Preferred (Bonus) Skills ● Robot Learning: Experience with imitationlearning (e.g., Diffusion Policy, ACT) , reinforcement learning, or behaviorcloning applied to physical robots. ● Sim-to-Real Expert: Prior experiencenavigating the challenges of sim-to-real transfer , including domainrandomization or gap adaptation. ● End-to-End Delivery: Experiencedelivering end-to-end robotics applications (e.g., advanced picking, assembly,or machine apprenticeship tasks).