Co-op Researcher – LLM Post-Training & Agentic Coding

Huawei Canada has an immediate Co-op opening for a Researcher.

About the team:

Founded in 2012 the Noahs Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The labs 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 companys products and services including Next Generation Foundation Model Agentic Models Physical AI and Reinforcement Learning.

About the job:

• Conduct cutting-edge research on LLM post-training reinforcement learning reasoning agentic coding and self-improving agents with the opportunity to take technically challenging ideas from initial hypothesis to rigorous experimental validation.

• Develop new approaches for agent training task and data synthesis environment generation scalable learning from interaction and agent evaluation with particular interest in terminal repository-level and software-engineering environments.

• Build and evaluate intelligent agents capable of reasoning tool use code generation and modification and solving complex real-world tasks while studying their capabilities failure modes and generalization behavior.

• Own research problems end-to-end including literature review problem formulation experiment design implementation large-scale evaluation analysis and communicating high-quality research findings.

• Build reliable and scalable infrastructure for LLM training inference agent rollout evaluation and synthetic data generation supporting rapid experimentation across large models and datasets.

• Improve the efficiency and reliability of GPU-based post-training and inference workflows including distributed execution resource utilization debugging automation and day-to-day research compute operations.

• Work closely with researchers and engineering teams across Huawei to rapidly prototype new ideas reproduce and extend state-of-the-art methods and translate promising research advances into practical LLM systems and downstream projects.

• Contribute in a fast-moving high-ownership research environment where a focused Co-op term can lead to meaningful research engineering open-source or publication-quality contributions.

About the ideal candidate:

• Currently enrolled in a senior Bachelors Masters or PhD program in Computer Science Machine Learning Artificial Intelligence or a related technical field with strong fundamentals in machine learning and modern deep learning.

• Strong understanding of large language models and modern post-training techniques including Transformer-based models supervised fine-tuning reinforcement learning preference optimization distillation reasoning or synthetic-data-driven training.

• Strong programming and software-engineering skills particularly in Python and PyTorch with the ability to independently implement debug profile and iterate on complex ML or agent systems.

• Hands-on experience in one or more relevant areas such as LLM post-training coding or tool-using agents reinforcement learning large-scale model training/inference distributed systems or ML infrastructure.

• Demonstrated ability to take an open-ended and ambiguous technical problem identify the important questions design effective experiments or systems and independently drive the work toward measurable results.

• Strong analytical and experimental judgment including the ability to design controlled experiments evaluate models rigorously diagnose unexpected behaviors and distinguish genuine improvements from experimental noise.

• Highly self-motivated intellectually curious and execution-oriented with strong communication and collaboration skills and the ability to operate effectively in a rapid research iteration cycle with substantial ownership.

• Strong assets include top-tier AI/ML publications or substantial research experience impactful open-source or agent/LLM projects and hands-on experience with frameworks and systems such as vLLM SGLang FSDP DeepSpeed Hugging Face Docker Linux Git or multi-GPU compute environments; we welcome candidates who are especially strong in either research or systems engineering and particularly value those who can bridge both.

• Applicants must be enrolled in a co-op program and be registered for an official co-op work term for the full duration of the employment period

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.