Principal Engineer Physical AI Systems Modeling
Renesas Electronics1 day ago
Ottawa, CanadaStaff+
Responsibilities
- Define the long-term Physical AI research direction and system architecture for humanoid and industrial robotics.
- Lead foundation model training and fine-tuning, vision-language-action model development, reinforcement learning, and generative modeling for end-to-end robotic systems.
- Architect and maintain virtual world models covering articulated hands and arms, sensors, robot dynamics, contact interactions, and environments.
- Own Physical AI data curation pipelines spanning simulation, synthetic data, real-world collection, teleoperation, multimodal synchronization, schemas, quality checks, versioning, and reproducible training and evaluation.
- Lead imitation learning, reinforcement learning, domain randomization, sim-to-real, and real-to-sim workflows, including evaluation and failure-mode analysis.
- Translate research into hardware implementations through algorithm–hardware co-design and validate against system-level requirements.
- Define system-level performance and resource trade-offs and guide deployment and technical priorities.
- Mentor engineers and researchers across multiple disciplines.
Requirements
- Ph.D. in Robotics, Engineering Science, Computer Science, Electrical Engineering, Computer Engineering, Mechanical Engineering, or a closely related field.
- At least 8 years of related research and engineering experience.
- Strong expertise in foundation model training and fine-tuning, vision-language-action models, reinforcement learning, generative models, and world models for Physical AI.
- Strong mathematics, physics, and software engineering background applied to complex robotic systems.
- Hands-on experience with NVIDIA Isaac Sim, MuJoCo, or similar physics-based simulation and virtual system modeling platforms.
- Deep expertise in robotics algorithms and system modeling, including kinematics, dynamics, perception, planning, control, grasping, manipulation, trajectory generation, and contact-rich interaction.
- Experience designing and implementing Physical AI data curation pipelines and sim-to-real and real-to-sim workflows.
- Experience translating research into hardware implementations through algorithm–hardware co-design.
- Experience with digital ASIC design and verification and collaboration across research, system architecture, RTL design, and verification.
- Strong software development experience in Python and C/C++ using CPU- and GPU-based environments, version control, testing, and reproducible experimentation.
- Publications in reputable robotics conferences and journals.
- Knowledge of neuromorphic and in-memory computing and modeling is advantageous.
- Knowledge of foundation model development and fine-tuning for hardware generation, such as schematics and RTL, is advantageous.
Benefits
- Competitive benefits package; further details are provided during the hiring process.
- Regular permanent employment.
- On-site role; remote work is not available.
Categories
ML EngineeringRobotics
About Renesas Electronics
Renesas Electronics designs and sells microcontrollers, analog and power ICs, and connectivity solutions for automotive, industrial, infrastructure and IoT manufacturers. Its portfolio spans embedded processing, power management and ADAS-ready components, sold directly and through channels to OEMs and Tier-1 suppliers, with software and development tools to integrate them. Founded in 2010 and headquartered in Tokyo, it is a public company on the Tokyo Stock Exchange and has expanded through acquisitions including Intersil and Dialog Semiconductor.