Co-op Researcher – Agentic AI

Huawei Canada; Markham; On-site; CAD 58,000 - 104,000; Part time

Job summary

Huawei Canada, based in Markham, invites applications for a co-op Researcher in the Noah’s Ark lab. You will join a small team pursuing frontier AI research on data-efficient agents and reinforcement learning for scalable applications.

Tasks include large-scale training runs, designing experiments, analyzing results, writing technical reports, and staying current with ML literature. Candidates are enrolled in a Bachelor’s or Master’s in CS or related field with strong Python and PyTorch skills.

Qualifications

• Must be enrolled in a Bachelor’s or Master’s in CS or related technical program.
• Solid understanding of ML principles and common DL architectures (Transformers, LSTM, CNN).
• Strong Python coding skills; experience building ML models/pipelines in PyTorch.

Responsibilities

• Participate in a small team of researchers on frontier AI research.
• Conduct large-scale training runs of deep learning models and deploy agentic pipelines.
• Design experiments and evaluations to test and understand new ideas.
• Develop custom analyses of experiments to diagnose issues and interpret results.
• Communicate findings to the team and suggest next steps.

Skills

Machine learning fundamentals

Python programming

Communication skills

Reading ML papers

Education

Bachelor’s or Master’s in Computer Science or related program

Tools

PyTorch

HuggingFace

DeepSpeed

vllm

Job description

Huawei Canada has an immediate Co-op opening for a 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 Next Generation Foundation Model, Agentic Models, Physical AI and Reinforcement Learning.

About the job:

• AI agents in the wild need to adapt to new environments with very limited interactions. The more novel the environment and fewer interactions, the more agents tend to lag behind humans. We are working on developing agents that explore and understand their environment in an extremely data-efficient manner. We are exploring new data-efficient architectures, meta-learning strategies for efficient test-time adaptation, and reinforcement learning algorithms.
• Participate in a small team of both junior and senior researchers on frontier research in AI agents.
• Conduct large scale training runs of deep learning models, deploy multi-component agentic pipelines, and design experiments and evaluations to effectively test and understand new ideas.
• Develop custom analyses of experiments to diagnose issues and understand results.
• Effectively communicate findings to team and proactively think about next steps for what can be improved;
• Actively participate in brainstorming sessions with the team.
• Help to write academic papers / technical report and run experiments to support them.
• Actively read new ML publications in areas related to our project.

The total target annual compensation for this position ranges from $58,000 to $104,000 depending on education, experience, and demonstrated expertise.

Job requirements

About the ideal candidate:

• Currently enrolled in a Bachelor’s or Master’s in Computer Science program or a related technical program.
• Knowledge of machine learning principles (training, regularization, generalization) and familiar with common deep learning architectures (e.g., Transformers, LSTM, CNN).
• Strong coding skills in Python, particularly developing machine learning models/pipelines in Pytorch.
• Strong verbal and written communication.
• Knowledge of machine learning techniques (diffusion, RL techniques (PPO, GRPO), JEPA, LLMs) is an asset.
• Experience with model training and popular tools (e.g., HuggingFace, DeepSpeed, vllm) is an asset, especially for training at scale for real applications.
• Experience in academic research is an asset.

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.