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Intern Engineer – RL Post-Training for LLMs

  • 4321 Still Creek Drive, Vancouver, BC, V5C 6S7, Canada

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. · Internship

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Job details

Pay
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.
Job type
Internship
Posting language
English
Posted

Original 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.


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

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.

More about this job

Responsibilities

Develop and optimize RL post-training pipelines for LLMs, focusing on reward modeling and GRPO. Build scalable systems for training, evaluation, and data generation while collaborating on cutting-edge research projects.

Requirements

Candidates must be Master's or Ph.D. students in Computer Science, AI, or a related field with a strong background in deep learning and RL. Proficiency in Python, PyTorch, and familiarity with LLM frameworks like Hugging Face or DeepSpeed is required.

Skills

  • Reinforcement Learning
  • Large Language Models
  • Python
  • PyTorch
  • GRPO
  • Reward Modeling
  • Transformer Architectures
  • Deep Learning
  • Distributed Training
  • Hugging Face
  • DeepSpeed
  • vLLM
  • SGLang
  • VeRL
  • Machine Learning
  • Data Generation Systems

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Science & Research
  • Engineering
  • Data & Analytics

Keywords

  • RL Post-Training
  • LLMs
  • GRPO
  • Reward Modeling
  • Transformer Architectures
  • Python
  • PyTorch
  • VeRL
  • Hugging Face
  • DeepSpeed
  • vLLM
  • SGLang
  • Distributed Training
  • AI Accelerators
  • Machine Learning
  • Deep Learning
  • Reinforcement Learning
  • GPU Architecture
  • Data Analytics
  • Software-Hardware Co-design

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