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

huaweicanada; Edmonton; On-site; CAD 34,000 - 55,000; Full time

Job summary

Huawei Canada is seeking a research-oriented co-op to advance state-of-the-art AI in areas like LLM post-training, agentic models, and reinforcement learning. You will design experiments, implement systems, and collaborate with researchers and engineers to translate findings into practical LLM systems.

The role emphasizes ownership of research problems, rigorous evaluation, and potential publications or open-source contributions within a fast-moving lab environment.

Qualifications

• Enrolled in senior Bachelor’s, Master’s, or PhD in CS/ML or related field.
• Strong fundamentals in ML and deep learning.
• Proficient in Python with PyTorch experience.
• Experience with LLM post-training, RL, or related areas.
• Ability to design experiments and communicate results clearly.

Responsibilities

• Conduct cutting-edge research on LLM post-training, RL, reasoning, and agentic coding.
• Develop approaches for agent training, data synthesis, and environment generation.
• Build and evaluate intelligent agents capable of reasoning, tool use, and code modification.
• Own research problems end-to-end from literature review to dissemination of results.
• Build scalable infrastructure for LLM training, inference, and evaluation.

Skills

Python

PyTorch

LLM post-training

Reinforcement learning

Distributed systems

Research in AI

Education

Senior Bachelor's/Master’s/PhD in CS/ML

Tools

vLLM

SGLang

FSDP

DeepSpeed

Hugging Face

Docker

Linux

Git

Job description

Huawei Canada is seeking a research-oriented co-op to advance state-of-the-art AI in areas like LLM post-training, agentic models, and reinforcement learning. You will design experiments, implement systems, and collaborate with researchers and engineers to translate findings into practical LLM systems.

The role emphasizes ownership of research problems, rigorous evaluation, and potential publications or open-source contributions within a fast-moving lab environment.