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-effective 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.