Overview Huawei Canada has an immediate 12-month contract opening for a Researcher.
About the team The Advanced Computing and Storage Lab, part of the Vancouver Research Centre, explores adaptive computing system architectures to address flexible and variable application loads. It aims to ensure stability and quality of training clusters, constructs dynamic cluster configuration strategy solvers, and establishes precision control systems to create stable and efficient computing power clusters. The lab focuses on industry AI scenarios such as large model training/inference, leveraging technologies like low-precision training, multimodal training, and reinforcement learning to analyze bottlenecks and develop optimization solutions that improve training and inference performance and usability.
About the job Aiming at key industry AI application scenarios such as large model training and inference, this role focuses on advancing performance, efficiency, and usability of AI systems on the Ascend platform. The work involves low-precision training, multimodal optimization, reinforcement learning, and training resource optimization to address system bottlenecks and deliver next-generation AI capabilities.
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.
The target annual compensation (based on 2080 hours per year) ranges from $78,000 to $150,000 depending on education, experience and demonstrated expertise.
About the ideal candidate Ph.D. or Masters degree in Computer Science, Computer Engineering or related fields (artificial intelligence, software, automation, electronics, communications, robotics, etc.).
Familiar with common model structures of large models such as DeepSeek and Llama, and have basic technical experience in large model training and inference optimization in LLM, MoE, multimodality, etc.
Familiar with hardware architecture and programming systems of AI accelerators such as GPUs/NPUs, and have experience in optimizing AI systems with coordinated software and hardware cores.
Assets/experience considered advantageous:
Solid programming foundation, familiar with Python/C/C++ programming languages, robust architecture design and coding practices.
Ability to work independently, robust communication, teamwork, willingness to adopt new technologies, and hands-on approach.
Experience in developing AI training frameworks and AI reasoning engines, or algorithm hardware and related experience.
Strong research capabilities in new technologies and architectures, ability to track cutting-edge AI technologies and contribute to system architecture innovation.

#J-18808-Ljbffr

📌 Researcher - AI Computing System (British Columbia)
🏢 huaweicanada
📍 British Columbia