Principal Engineer Physical AI Systems Modeling at Renesas Electronics — NeverHard

Job Description We are seeking a Principal Engineer (Physical AI - System Modeling) to join the Pathfinding Studio within the Engineering Innovation Office. As a technical thought leader and principal contributor, you will define and lead research in foundation model training and fine-tuning, vision-language-action models, reinforcement learning, and generative models. You will lead multidisciplinary work spanning virtual system modeling, data curation, learning, perception, planning, control, and translation of research into hardware implementations. With broad latitude to define objectives and technical approaches, you will solve critical problems under uncertainty, guide long-term technology and intellectual property initiatives and deliver outcomes that influence multiple functions and future products. Responsibilities: · Define the long-term Physical AI research direction and system architecture for humanoid and industrial robotics to guide next generation technology and product strategy. · Identify emerging methods and lead foundation model training and fine-tuning, VLA development, reinforcement learning, and generative modeling, connecting perception, planning, control, and manipulation in end-to-end robotic systems. · Architect, develop, and maintain virtual system world models including articulated hands and arms, sensors, robot dynamics, contact interactions, and environments. · Own the technical architecture of Physical AI data curation pipelines, spanning simulation, synthetic data generation, real-world collection and teleoperation, multimodal synchronization, schemas, quality checks, dataset versioning, and reproducible training and evaluation. · Lead imitation-learning, reinforcement-learning, domain-randomization, sim-to-real, and real-to-sim workflows; establish evaluation criteria, analyze failure modes, and resolve complex model and controller issues with limited information. · Translate research into hardware implementations through algorithm–hardware co-design. · Define and evaluate system-level performance and resource trade-offs to guide deployment decisions and long-term technical priorities. · Mentor engineers and researchers across domains.