Huawei Canada is hiring a Researcher to advance performance, efficiency, and usability of AI systems on its Ascend platform. The role develops optimization solutions for AI training and inference, improves computing systems and training clusters, and contributes to large-model acceleration and AI computing architecture research.
Responsibilities
- 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
