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Huawei Canada has an immediate 4-12 month internship opening for an Intern Researcher.

About the team

Established in 2014, the Distributed Scheduling and Data Engine Lab is Huawei Cloud's technical innovation center in Canada. The lab focuses on researching and developing advanced cloud technologies, supporting the productization and iterative optimization of its technical achievements. Current research areas include cloud native databases, intelligent SQL engine, LLM/Agent infrastructure and LLM/Agent Evaluation Technology. The lab fosters a robust technical environment, allowing collaboration with industry experts to create a highly competitive cloud platform.

About the Job

• Contributing to cutting-edge research on the capabilities and evaluation of large language model-based agents to shape the next generation of reliable, reasoning-capable AI systems.
• The role focuses on addressing fundamental questions that arise as AI agents are increasingly deployed in complex, long-horizon tasks, specifically regarding how they manage context, reason under uncertainty, and maintain reliable behavior.
• Research strategies will involve drawing from formal reasoning, causal inference, and theoretical foundations to better characterize and understand agent behavior in various environments.
• This is strictly a research-first position where you will be expected to formulate complex problems, run detailed experiments, and contribute to publishable work rather than building production systems.
• The ultimate goal of the work is to develop principled methods and theoretical foundations that ensure meaningful progress in the field of agent evaluation and autonomous reasoning.

The targeted annual total compensation (based on 2,080 hours per year) ranges from $58,000 to $104,000 depending on education, experience, and demonstrated expertise.

About the ideal candidate

• Currently enrolled in a Master or PhD program in Computer Science, Electrical Engineering, Mathematics, or a closely related field
• Demonstrated research output such as publications, preprints, or workshop papers at top-tier venues.
• Solid foundation in machine learning and NLP fundamentals. Research experience in one or more of the following areas is an asset: LLM Architectures, causal inference and counterfactual reasoning, Formal Methods, or Neurosymbolic AI
• Demonstrated ability to work on a research project from hypothesis to result
• Clear and effective written and verbal communication skills
• Experience designing, critiquing, or extending LLM reasoning or agent evaluation benchmarks