Senior Researcher – 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 · CA$127,000–CA$225,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Shape foundation-model designs for LLM, code and multimodal work, working with internal and external groups
- Improve post-training and continual-training methods to build capable models that use hardware efficiently
- Set infrastructure needs for large-scale distributed training and inference, including parallelization and operator fusion
- Study computation patterns in common model designs to inform AI hardware and infrastructure development
What you bring
- You have trained and optimized advanced AI models, including training and deploying models at a scale of ten billion parameters or more
- You understand modern AI architectures, algorithm behavior, framework implementation, and distributed training and inference methods
- You are familiar with AI chip designs such as GPUs, NPUs or TPUs
Who this fits
This senior research role suits someone focused on AI systems, model training and hardware efficiency. It is an on-site, full-time position in Markham. A PhD in a related field and strong publications in AI systems or chip design are listed as assets.
From the employer
Huawei Canada has an immediate permanent opening for a Senior Researcher.
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.
The total target annual compensation for this position ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise.
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 in AI architecture, computer architecture, or related fields is an asset.
Solid publication records in the field of AI systems or chip design is 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.