Senior Researcher – Hardware Efficient AI Foundation Model Training

Job description

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.

Job requirements

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.