Researcher - AI Computing System

Huawei Technologies Canada Co., Ltd.•Burnaby, BC

About The Position

Huawei Canada has an immediate 12-month contract opening for a Researcher within the Advanced Computing and Storage Lab. This lab, part of the Vancouver Research Centre, focuses on exploring adaptive computing system architectures to handle flexible and variable application loads. The role aims to ensure the stability and quality of training clusters, develop dynamic cluster configuration strategy solvers, and establish precision control systems for efficient computing power clusters. A key goal is to address industry AI application scenarios like large model training/inference using technologies such as low-precision training, multi-modal training, and reinforcement learning. This involves bottleneck analysis and the design and development of optimization solutions to enhance training and inference performance and usability on the Ascend platform.

Requirements

Ph.D or Masters degree in Computer Science, Computer Engineering majors in artificial intelligence, computer science, software, automation, electronics, communications, robotics, etc.
Familiar with the common model structures of large models such as Deepseek and Llama, and have basic technical accumulation in large model training and inference optimization in the fields of LLM, MoE, multimodality, etc.
Familiar with the hardware architecture and programming system of AI accelerators such as GPU/NPU, and have experience in optimizing AI systems with coordinated software and hardware cores.

Nice To Haves

Solid programming foundation, familiar with Python/C/C++ programming languages, good architecture design and programming habits
Ability to work independently and solve problems, good at communication, willing to cooperate, keen on new technologies, good at summarizing and sharing, and like hands-on practice
Experience in the development of AI training frameworks and AI reasoning engines, or algorithm hardware and related experience
Strong research capabilities in new technologies and new architectures, can quickly track and gain insights into the most cutting-edge AI technologies in the industry, and lead the continuous leadership of system architecture innovation.

Responsibilities

Responsible for design and development of optimization solutions for AI training and inference systems, with a focus on FP8 optimization, RL-driven training agents, multimodal reinforcement learning or next-generation multi-modal understanding & generation.
Combine AI algorithm requirements with system-level architectural optimization in computing, I/O, scheduling, and precision control to improve performance.
Build stable, efficient AI training clusters, leveraging dynamic cluster configuration and precision control to ensure scalability and reliability.
Develop software frameworks, operator libraries, acceleration libraries, and system-level optimizations for NPU platforms to accelerate large-model AI training.
Drive innovation in optimizing large-model training and inference with low-precision training, parallel strategy tuning, and reinforcement learning.
Grasp the latest research progress and technological trends in AI computing cluster architecture design, training acceleration, and inference acceleration across academia and industry to strengthen the competitiveness of AI computing cluster systems.