Machine Learning Researcher -LLM Agents & Efficient Deep Learning

Huawei Technologies Co.; Montreal (administrative region); Hybrid; CAD 148,000 - 218,000; Full time

Job summary

Huawei Canada is seeking a Machine Learning Researcher for a 12-month opening in the Noah’s Ark Lab. The role focuses on multi-agent systems, scalable SLM pipelines, and advances in AI research to inform product and service innovation.

You will work on training, inference optimization, and integration with tool-use frameworks, with a strong emphasis on publications and deep learning expertise.

Qualifications

• MSc or PhD in Computer Science, Electrical Engineering, or related field.
• Strong publication record in top AI conferences (e.g., NeurIPS, ICML, AAAI, ICLR).
• Strong background in deep learning and optimization.
• Experience with transformer architectures, LLMs, and SLMs.
• Proficiency in PyTorch.
• Solid understanding of linear algebra and probability, and optimization algorithms (SGD, Adam).
• Experience implementing and scaling ML systems in production or research settings.

Responsibilities

• Build scalable SLM pipelines for training, evaluation, and deployment.
• Develop methods for efficient training and inference (quantization, pruning, sparsity).
• Explore parameter-efficient fine-tuning such as LoRA; memory and long-context modeling.
• Prototype new ideas and validate through rigorous experimentation.
• Optimize inference performance (latency, throughput, KV-cache).
• Integrate reasoning models with tool-use frameworks (function calling, APIs).

Skills

Pytorch

Transformer architectures

LLMs

SLMs

Deep learning

Optimization

Python

Research publication

Education

MSc or PhD in Computer Science, Electrical Engineering, or related field

Job description

Huawei Canada has an immediate 12months opening for a Machine Learning Researcher.

About the Team：

Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving.

About the Job:

• multi-agent systems, and function call models. Build scalable SLM pipelines for training, evaluation, and deployment.
• Develop methods for efficient training and inference, including: Quantization (e.g., low-precision formats, INT8/FP8); Pruning and sparsity; Low-rank and tensor decompositions
• Explore advances in: Parameter-efficient fine-tuning such as LoRA; Memory and long-context modeling; Adapt GPU-based LLMs for CPU-based SLMs.
• Prototype new ideas and validate them through rigorous experimentation
• Optimize inference performance (latency, throughput, KV-cache efficiency)
• Integrate reasoning models with tool-use frameworks (e.g., function calling, APIs)

The total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience, and demonstrated expertise.

• MSc or PhD in Computer Science, Electrical Engineering, or related field
• Strong publication record in top AI conferences such as NeurIPS, ICML, AAAI, ICLR, etc.
• Strong background in deep learning and optimization
• Experience with transformer architectures, LLM, SLM
• Proficiency in Pytorch
• Solid understanding of: Linear algebra and probability; Optimization algorithms (SGD, Adam, etc.)
• Experience implementing and scaling ML systems in production or research settings.

Additional Information

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.