# Chief Scientist - Physical AI at Huawei Technologies Canada Co., Ltd. > Onsite · Canada · Director+ · Other · open. Source: https://alderhq.app/jobs/recruitee%3Ahuaweicanada%3A2579882 (read 2026-11-08). ## Facts (observed) - Company: [Huawei Technologies Canada Co., Ltd.](https://alderhq.app/companies/huawei-technologies-canada-co-ltd) - Location text: Ottawa, Ontario, Canada - Where it can be done from (inferred): Onsite · Canada (evidence: "Location: Ottawa, Ontario, Canada") - Posted: 2026-04-23 (199 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://huaweicanada.recruitee.com/o/chief-scientist-physical-ai-1 - Skills listed (inferred): Python, PyTorch, TensorFlow, LLMs ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Canada, n 144): p25 CAD 145k, median CAD 176k, p75 CAD 235k ## Description (the employer's text) Huawei Canada has an immediate permanent opening for a Chief Scientist. About the team: Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving. ### About the job Work at the forefront of Embodied AI and Spatial AI, collaborating with researchers and engineers to solve real-world challenges and bridge cutting-edge research with scalable deployment. Develop state-of-the-art approaches in Physical AI, including robotic foundation models, world models, multimodal representation learning, reasoning and planning, as well as imitation and reinforcement learning with integrated low-level control. Translate advanced problem formulations and model designs into efficient, production-grade systems, enabling rapid iteration through large-scale experimentation. Design and execute experiments across both simulation and real-world robotic platforms, covering data collection, evaluation, and performance analysis. Drive closed-loop learning by integrating data, models, and real-world feedback to continuously improve system robustness and adaptability. Collaborate cross-functionally across research, infrastructure, and robotics systems to ensure end-to-end system coherence and scalability. Contribute to and lead high-impact research outputs, including patents and publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, and ICRA. ### Requirements ### About the ideal candidate PhD in Computer Science, Robotics, AI, or a related field (or Master’s with equivalent research and industry experience). Strong foundation in generative AI and sequence modeling, particularly in vision-language or multimodal learning systems. Proven research track record with first-author publications in top-tier conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or ICRA. Solid expertise in modern deep learning frameworks (e.g., PyTorch, TensorFlow) and strong proficiency in Python. Hands-on experience with robotics systems, including decision-making, planning, and control; experience with real-world robots or ROS is an asset. Experience working with large-scale datasets, sensor data, and advanced architectures such as transformers or diffusion models. ### Additional Information Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know, and we will work with you to meet your needs. All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates. --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/recruitee%3Ahuaweicanada%3A2579882 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.