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Machine Learning Researcher -LLM Agents & Efficient Deep Learning

  • Montreal, QC, H3N 1X9, Canada

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

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Job details

Pay
The total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience, and demonstrated expertise.
Job type
Full-time
Posting language
English
Posted

Original 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.

More about this job

Responsibilities

Develop and optimize efficient deep learning methods for LLMs and SLMs, focusing on quantization, pruning, and parameter-efficient fine-tuning. Integrate reasoning models with tool-use frameworks and build scalable pipelines for training and deployment.

Requirements

Requires an MSc or PhD in Computer Science or Electrical Engineering with a strong publication record in top AI conferences. Must possess deep expertise in Pytorch, transformer architectures, and mathematical optimization.

Skills

  • Machine Learning
  • LLM Agents
  • Deep Learning
  • Pytorch
  • Transformer Architectures
  • Quantization
  • Pruning
  • LoRA
  • Linear Algebra
  • Probability
  • Optimization Algorithms
  • Multi-agent Systems
  • Function Calling
  • SLM Pipelines
  • Inference Optimization
  • GPU/CPU Adaptation

Visa sponsorship

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Categories

  • Science & Research
  • Technology
  • Software
  • Engineering
  • Data & Analytics

Keywords

  • Machine Learning
  • LLM
  • SLM
  • Deep Learning
  • Pytorch
  • NeurIPS
  • ICML
  • AAAI
  • ICLR
  • Quantization
  • Pruning
  • Sparsity
  • LoRA
  • KV-cache
  • Multi-agent Systems
  • Function Calling
  • Linear Algebra
  • SGD
  • Adam
  • Transformer
  • NLP
  • Computer Vision
  • RL
  • AI Theory
  • Autonomous Driving
  • Inference Performance
  • Tensor Decompositions

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