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 Principal Engineer (Physical AI - System Modeling) to join the Pathfinding Studio within the Engineering Innovation Office. As a 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.

Qualifications

• Extensive relevant research and engineering experience, including 8 years of related experience with a Ph.D.
• Strong expertise in foundation model training and fine-tuning, vision-language-action models (VLAs), reinforcement learning, and generative and world models targeting Physical AI systems such as humanoids and industrial robotics.
• Strong background in mathematics, physics, and software engineering, with the ability to apply rigorous modeling, analysis, and engineering methods to complex robotic systems.
• Knowledge in Neuromorphic and in-memory computing and modeling will be considered an advantage.
• Knowledge with foundation model development and finetuning for specification to hardware generation (e.g., schematics, RTL) will be considered an advantage.
• Hands-on experience with NVIDIA Isaac Sim, MuJoCo, and 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, including simulation and real-world data collection, teleoperation, synthetic data, multimodal data quality, and sim-to-real and real-to-sim workflows.
• Prior experience translating research into hardware implementations, including algorithm–hardware co-design and validation against system-level requirements.
• Experience in digital ASIC design and verification and in collaborating across research, system architecture, RTL design, and verification disciplines.
• Strong software development experience in Python and C/C++ using CPU- and GPU-based environments, version control, testing, and reproducible experimentation.
• Publications in reputed conference and journals related to robotics.

Education:

A Ph.D. in Robotics, Engineering Science, Computer Science, Electrical Engineering, Computer Engineering, Mechanical Engineering or a closely related field.

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