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 efficient 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
Robust deep learning and optimization background
Experience with transformer architectures, LLMs, and SLMs
Proficiency in PyTorch
Solid publication record in top AI conferences
Apply research expertise in PyTorch, optimization, and productive inference to Huawei Canada’s LLM and SLM development.
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