Staff Engineer Physical AI Systems

We are seeking a Staff Engineer (Physical AI - System Engineering) to join the Pathfinding Studio within the Engineering Innovation Office. In this role, you will develop and validate technologies related to Physical AI systems models and dataflow for humanoid and industrial robotics. The role combines hands-on design, virtual and hybrid system modeling, algorithm–hardware co-design, experimental validation, and cross-disciplinary collaboration to achieve project milestones. Responsibilities: · Contribute to Physical AI related technology development for humanoid and industrial robotics, translating requirements into hardware, mechanical, compute, and software design choices. · Design, prototype, integrate, and validate humanoid hardware and mechanical subsystems, including articulated hands and arms, actuator and sensor integration, and electromechanical interfaces. · Develop and evaluate learning-based perception, planning, control, and manipulation workflows, including foundation models, vision-language-action models, imitation learning, and reinforcement learning as appropriate to project objectives. · Develop and maintain virtual system models and robotics hardware and software infrastructure for the lab environment. · Evaluate and prototype neuromorphic and similar ultra-low-power computing approaches for humanoid sensing, perception, and control; integrate candidate computing platforms into system demonstrations. · Develop and apply best practices for reproducible simulation, data collection, training, and evaluation; investigate complex failure modes through in-depth analysis of variable factors and validate sim-to-real transfer against system requirements. · Six years of related research and engineering experience with a Master’s degree, or three years with a PhD, demonstrating specialized depth and/or breadth of expertise and recognition as a technical expert within a team or department. · Strong foundation in mathematics, physics, and robotics, including kinematics, dynamics, perception, planning, control, grasping, manipulation, and contact-rich interaction. · Hands-on experience with NVIDIA Isaac Sim, MuJoCo, or comparable physics-based simulation and virtual system modeling platforms, including validation against physical hardware. · Hands-on experience with mechanical CAD and electronic subsystem and PCB design and tools targeting robotics. · Experience with learning-based robotics methods, such as foundation model adaptation, vision-language-action models, imitation learning, reinforcement learning, or generative and world models. · Experience with simulation and real-world data collection, data quality, reproducible evaluation, and sim-to-real workflows; strong Python and C/C++ development skills in embedded, and distributed CPU-, or GPU-based environments. · Experience with electromechanical integration, actuator and sensor selection and sensor modeling, and evaluation of mechanical constraints for articulated hands, arms, or other humanoid mechanisms. · Knowledge in Neuromorphic and in-memory computing and modeling considered an advantage. · Ability to measure and analyze system-level trade-offs in power and energy consumption, latency, compute resources, thermal behavior, and task performance. · Experience translating algorithms into hardware implementations and collaborating with electronics, system architecture, RTL design, and verification teams; digital ASIC design and verification experience is an asset. Education: Master’s degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Engineering, Computer Science or a closely related field is the minimum educational requirement. PhD in a relevant field is preferred. Renesas is an embedded semiconductor solution provider driven by its Purp