Huawei Canada
Technology
InternEngineer–RLPost-TrainingforLLMs
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“Intern Engineer – RL Post-Training for LLMs at Huawei Canada. Skills: Reinforcement learning, LLMs, Post-training. Develop RL post-training pipelines. Optimize RL post-training pipelines”
What You'll Achieve.
Enhance algorithm performance; Enhance training efficiency
Industry & Context.
Problem-solving skills
What They're Looking For.
Must Have
Master or Ph. D. student, Machine learning background, Reinforcement learning background, Deep learning background, Familiarity with LLMs, Familiarity with transformer architectures, Familiarity with post-training methods, Proficiency in Python, Proficiency in PyTorch, Proficiency in LLM frameworks
Nice to Have
Hands-on experience with LLMs, Hands-on experience with RL training algorithms, Familiarity with RL frameworks, Experience with Hugging Face, Experience with DeepSpeed, Experience with vLLM, Experience with SGLang, Experience with distributed training frameworks, Experience with large-scale experimentation, Experience with LLM infrastructure
What You'll Do.
Develop RL post-training pipelines
Optimize RL post-training pipelines
Improve model performance
Improve model reasoning
Improve model alignment
Build scalable training systems
Build evaluation systems
Build data generation systems
Collaborate with researchers
Collaborate with engineers
Stay current with advancements
How You'll Work.
Team & Collaboration
Cross-functional teams
Communication Scope
Communication skills
Full Job Description
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. ## Requirements 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
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