Principal Engineer Physical AI Systems Modeling
- Employment type
- Full-time · On-site
- Posted
- November 10, 2026
- Province
- Ontario (ON)
Hiring confidence: Sparse posting · 7/100
This posting is missing most of the signals we look for, or shows patterns common to listings that are not actively being filled. Check with the employer before applying. How this is scored
Job Overview
This role is based in Ontario. Renesas Electronics is hiring for this full-time position through Jobily, where you can check how well your resume matches the posting before you apply.
Market context
About this role in Ontario
Average salary for Principal Engineer Physical AI Systems Modeling in Ontario
We do not have a salary benchmark for this title in Ontario yet. The posting does not state a salary either.
NOC code
This posting has not been matched to a NOC 2021 unit group yet. Use the NOC finder to identify the code from the duties listed above.
Typical qualifications for Canadian employers
- Principal contributor level expertise
- Strong background in AI and machine learning
- Experience with foundation models and fine-tuning
- Knowledge of reinforcement learning
- Experience in vision-language-action models
Fit check
Is this role right for you?
Top skills this posting asks for:
- Foundation model training
- Fine-tuning models
- Vision-language-action models
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What you'll do
- Define and lead research in foundation model training and fine-tuning
- Lead work on vision-language-action models
- Drive reinforcement learning initiatives
- Oversee generative models development
- Lead multidisciplinary work spanning virtual system modeling
- Manage data curation efforts
- Lead perception system development
- Drive planning and control research
- Translate research into practical applications
What you'll need
- Principal contributor level expertise
- Strong background in AI and machine learning
- Experience with foundation models and fine-tuning
- Knowledge of reinforcement learning
- Experience in vision-language-action models
- Understanding of generative models
- Multidisciplinary collaboration capability