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 productive 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.

📌 Machine Learning Researcher LLM Efficiency (Winnipeg)
🏢 Huawei Technologies Canada Co.
📍 Winnipeg