Intern Engineer – RL Post-Training for LLMs

Huawei Canada has an immediate 6-12 months internship opening for an Intern Researcher.

About the team:

The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications.

One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.

About the job:

• Develop and optimize RL post-training pipelines for LLMs (e.g., GRPO, reward modeling).
• Conduct experiments to improve model performance, reasoning, and alignment.
• Build scalable training, evaluation, and data generation systems.
• Collaborate with researchers and engineers on cutting-edge LLM projects
• Stay current with advancements in RL, LLMs, and post-training research.

The total target annual 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:

• Enrolled as Master or Ph.D. student in Computer Science, AI, or related field.
• Strong background in machine learning, reinforcement learning, and deep learning. Familiarity with Large Language Models, transformer architectures, and post-training methods.
• Proficiency in Python, PyTorch, and LLM frameworks.
• Hands-on experience with LLMs and RL training algorithms (e.g., GRPO) is an asset.
• Familiarity with RL frameworks, such as VeRL.
• Experience with open-source LLM frameworks such as Hugging Face, DeepSpeed, vLLM, or SGLang is an asset.
• Knowledge of domain-specific languages used with AI accelerators.
• Experience with distributed training frameworks, large-scale experimentation, or LLM infrastructure is an asset.
• Strong problem-solving and communication skills