Senior Researcher – Hardware Efficient AI Foundation Model Training

About the role

Huawei Canada’s Computing Data Application Acceleration Lab is seeking a Senior Researcher to drive hardware‑efficient training of foundation AI models. The role focuses on designing next‑generation model architectures and optimizing large‑scale distributed training and inference pipelines.

Key responsibilities

• Lead the design of foundational model architectures for LLM, code, and multimodal sub‑fields, delivering breakthroughs in post‑training and continual training.
• Develop state‑of‑the‑art models that combine high performance with hardware efficiency, and demonstrate industry impact.
• Define technical requirements for large‑scale distributed training and inference, including parallelization strategies and operator fusion, and evaluate computational characteristics of emerging architectures.

Required profile

• Proven experience training and optimizing AI models at 10 B+ parameters.
• Deep knowledge of modern AI architectures such as long‑sequence models, reinforcement learning, multimodal systems, and autonomous agents.
• Strong understanding of AI algorithm mechanisms and AI framework implementations (e.g., PyTorch, vLLM, SGLang).
• Familiarity with AI chip architectures (GPU, NPU, TPU) and memory hierarchy/interconnect technologies (asset).
• PhD or strong publication record in AI systems or chip design (asset).

Required skills

• AI model training and optimization at massive scale.
• Proficiency with PyTorch, vLLM, and SGLang.
• Distributed training and inference techniques, including parallelization and operator fusion.
• Experience with GPU, NPU, and TPU hardware platforms.
• Understanding of memory hierarchy and interconnect technologies.