Senior Researcher – Hardware Efficient AI Foundation Model Training

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.