Senior Researcher – Hardware Efficient AI Foundation Model Training

• 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.

• 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.

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.