Additionalinformation

Additional Information

Renesas is an embedded semiconductor solution provider driven by its Purpose, To Make Our Lives Easier. With a global team of over 21,000 engineers and problem solvers in more than 30 countries, we offer the opportunity to work on world‑leading technology for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and smarter future.

At Renesas, TAGIE is our culture, grounded in being Transparent, Agile, Global, Innovative, and Entrepreneurial. It shapes how we work, grow and deliver on our purpose together. This collaborative spirit and mindset drive our semiconductor technology to transform industries and impact millions of lives.

We believe in rewarding our employees with a competitive benefits package alongside their salary. More information will be provided during the hiring process.

Are you ready to join our team and shape the future with us?

Export Control Compliance Condition

Renesas Canada must at all times comply with U.S. and Canadian export control legislative requirements. Current U.S. export control laws require that Renesas Canada obtain a U.S. government export license prior to employing persons in positions with access to certain technology. All positions at Renesas Canada have direct or indirect access to this technology. As a result, it is necessary for Renesas Canada to determine if it would need an export license for new employees. To do so, we ask all candidates for employment whether their most recent country of citizenship or permanent residence is one of the countries specified in the export controls laws or regulations.

Renesas recognizes that you are entitled to human rights and privacy protections in the workplace and takes these obligations seriously. As such, we want you to know that your decision to complete the export control questions is entirely voluntary. Regrettably, should you choose not to complete these questions, we cannot consider you for employment because doing so would jeopardize our ability to comply with the applicable export controls laws. Should you choose to complete the questions, we will use the information collected only to determine if we would need an export license to employ you and not for any other purpose. 

Successful candidates must be eligible to meet the requirements of U.S. export control regulations, including (if applicable) the issuance of any required export license prior to starting employment at Renesas Canada. The decision whether to or not to file or pursue an export license application is at the sole discretion of Renesas Canada.

Companydescription

Company Description

Jobdescription

Job Description

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.

Qualifications

• Six years of related research and engineering experience with a Master’s degree, or three years with a PhD.
• 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.
• Publications in reputed conference and journals related to robotics.

Education:

Master’s degree (PhD preferred) in Robotics, Mechanical Engineering, Electrical Engineering, Computer Engineering, Computer Science or a closely related field.

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