Principal Architect – Hardware Efficient AI Foundation Model Training
Huawei Technologies Canada Co., Ltd. Open · verified Oct 22, 2026 At a glance
Senior AI Research role at Huawei Technologies Canada Co., Ltd. Markham · full-time · no salary stated · we estimate CA$151,100–CA$175,975 across Canada.
Huawei Technologies Canada Co., Ltd. has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Lead architecture design for foundation models across LLM, code and multimodal work, advancing post-training and continual training
- Build models that combine strong performance with efficient use of hardware
- Set infrastructure needs for large-scale training and inference, including parallelization and operator fusion
- Study compute demands of representative model architectures to guide AI hardware and infrastructure development
What you bring
- You have trained and optimized advanced AI models, including deployment at a scale of ten billion or more parameters
- You understand current approaches such as long-sequence models, reinforcement learning, multimodal systems and agents
- You can explain AI algorithm mechanisms and work with framework internals and distributed training and inference methods
- You understand AI chip designs, including GPUs, NPUs or TPUs
Who this fits
This senior architect role suits someone focused on AI model systems, training efficiency and hardware-aware design. It is an onsite position in Markham, Canada.
From the employer
Huawei Canada has an immediate permanent opening for a Principal Architect.
About the team:
The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications.
One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.
About the job:
Collaborate with internal and external organizations to lead the design of foundational model architecture for LLM/Code/Multimodal subfields by breakthroughs in post-training and continual training. Develop a foundational model with state-of-the-art performance and hardware efficiency, and establish industry impact.
Propose the technical requirements for large-scale distributed training and inference infrastructures such as parallelization and operator fusion, analyze the computational characteristics of typical architectures, and ensure the accuracy and advancement of AI hardware & infrastructure evolution.
About the ideal candidate：
Experience in training and optimizing cutting-edge AI models/applications, especially in training and deploying AI models at a scale of 10B+ parameters.
Proficiency in the latest AI architecture (such as long-sequence, reinforcement learning, multimodal, and agents). Deep understanding of AI algorithm mechanisms.
Solid command of the underlying implementation of AI frameworks (such as PyTorch, vLLM, and SGLang), and mainstream distributed training and inference techniques.
Familiarity with AI chip architecture (such as GPU, NPU, and TPU). Understanding of memory hierarchy and interconnect technologies is an asset.
PhD preferred in AI architecture, computer architecture, or related fields.
Solid publication records in the field of AI systems or chip design are an asset.
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.