Advance LLM efficiency at Huawei Canada through quantization, pruning, and scalable small language model pipelines. This 12-month opening supports research in Canada. As a Machine Learning Researcher with Noah’s Ark lab, you will investigate effective deep learning for language models and agents. The work spans multi-agent systems, function call models, transformer architectures, and deployment. You will prototype ideas, validate experiments, and connect research with scalable machine learning systems. Key Responsibilities:
Build scalable SLM training, evaluation, and deployment pipelines Develop quantization, pruning, sparsity, and decomposition methods Explore LoRA and memory-efficient long-context modeling Optimize latency, throughput, and KV-cache efficiency Integrate reasoning models with function-calling frameworks Requirements:
MSc or PhD in computer science, electrical engineering, or related field Strong deep learning and optimization background Experience with transformer architectures, LLMs, and SLMs Proficiency in PyTorch Strong publication record in top AI conferences Apply research expertise in PyTorch, optimization, and efficient inference to Huawei Canada’s LLM and SLM development.