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Huawei Technologies Canada Co., Ltd.

Researcher - Reinforcement Learning

Montréal, QC, Canada
Edmonton, AB, Canada
Markham, ON, Canada

Apply on huaweicanada.recruitee.com About employer

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Job details

Posting language: English · French
Posted: Nov 27, 2025

Original job description

English section of this bilingual listing.

Huawei Canada has an immediate 12-month contract opening for a Reinforcement Learning Researcher.

About the team:

Founded in 2012, the Noah’s Ark lab has evolved into a prominent research organization with notable achievements in academia and industry. The lab’s mission focuses on advancing artificial intelligence and related fields to benefit the company and society. Driven by impactful, long-term projects, the aim is to enhance state-of-the-art research while integrating innovations into the company's products and services, including LLMs, RL, NLP, computer vision, AI theory, and Autonomous driving.

About the job:

Enabling Large Language Models (LLMs) to learn from experience, interaction, and environment feedback, moving beyond static fine-tuning toward continual, agentic self-improvement.
LLM post-training paradigms (e.g., RLHF, GRPO, reward-free methods, etc.).
Agentic reinforcement learning for tool-using and browsing-based LLMs trained in interactive environments.
Agentic evaluation and benchmarking, including design of multi-turn, verifiable reasoning tasks.
Your work will involve implementing and evaluating new training and evaluation pipelines for reasoning-enhanced LLMs and tool-using agents, scaling experiments on large GPU clusters, and contributing to scientific insights and publications in this emerging area.

About the ideal candidate:

PhD degree in Computer Science or related fields or master's degree with comparable experience.
Strong foundation in deep learning, including architectures such as Transformers and optimization techniques for large models.
Practical or research experience in reinforcement learning, self-supervised learning, or language model fine-tuning.
Proven research record in AI by having at least one paper as the first author in top tier venues, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA.
Solid proficiency in Python and experience with PyTorch, DeepSpeed, Megatron and other distributed training frameworks.
Familiarity with LLM post-training pipelines (RLHF, GRPO/PPO, SFT, LoRA, MoE, etc.) is an asset.
Experience with multi-agent RL, tool-use / browser/coding agents, is an asset.
Strong communication and writing skills; enthusiasm for open research and collaborative problem-solving.

Read the full bilingual original

Huawei Canada has an immediate 12-month contract opening for a Reinforcement Learning Researcher.

About the team:

About the job:

Enabling Large Language Models (LLMs) to learn from experience, interaction, and environment feedback, moving beyond static fine-tuning toward continual, agentic self-improvement.
LLM post-training paradigms (e.g., RLHF, GRPO, reward-free methods, etc.).
Agentic reinforcement learning for tool-using and browsing-based LLMs trained in interactive environments.
Agentic evaluation and benchmarking, including design of multi-turn, verifiable reasoning tasks.
Your work will involve implementing and evaluating new training and evaluation pipelines for reasoning-enhanced LLMs and tool-using agents, scaling experiments on large GPU clusters, and contributing to scientific insights and publications in this emerging area.

About the ideal candidate:

PhD degree in Computer Science or related fields or master's degree with comparable experience.
Strong foundation in deep learning, including architectures such as Transformers and optimization techniques for large models.
Practical or research experience in reinforcement learning, self-supervised learning, or language model fine-tuning.
Proven research record in AI by having at least one paper as the first author in top tier venues, such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA.
Solid proficiency in Python and experience with PyTorch, DeepSpeed, Megatron and other distributed training frameworks.
Familiarity with LLM post-training pipelines (RLHF, GRPO/PPO, SFT, LoRA, MoE, etc.) is an asset.
Experience with multi-agent RL, tool-use / browser/coding agents, is an asset.
Strong communication and writing skills; enthusiasm for open research and collaborative problem-solving.

Informations supplémentaires :

Huawei Canada s'engage à un processus de recrutement équitable, inclusif et accessible. Si vous avez besoin d'un accommodement à n'importe quelle étape du processus d'embauche, veuillez nous en informer, et nous travaillerons avec vous pour répondre à vos besoins.

Toutes les candidatures pour ce poste sont examinées directement par notre équipe de recrutement, nous n'utilisons pas d'outils d'intelligence artificielle pour filtrer ou sélectionner les candidats.

Huawei vise à soutenir un environnement de travail francophone pour ses employés au Québec. Nous avons pris des mesures pour éviter de demander une autre langue que le français pour ce poste. Cependant, la maîtrise de l'anglais est essentielle pour ce rôle pour les raisons suivantes :

La personne devra communiquer régulièrement avec des collègues situés en dehors du Québec, où l'anglais est la langue principale utilisée pour la communication entre les bureaux. De plus, la nature des tâches liées à ce poste, qui relève d'un domaine hautement spécialisé de l'intelligence artificielle, nécessite également une connaissance de l'anglais.

L'utilisation du genre masculin a été adoptée afin de faciliter la lecture et n'a aucune intention discriminatoire.

Apply on huaweicanada.recruitee.com About employer

More about this job

Responsibilities

The Reinforcement Learning Researcher at Huawei will design and test training methods that let language models learn from feedback and interaction. The work includes running experiments on large computer clusters and contributing to research papers.

Requirements

Candidates need a PhD or master's in computer science, deep learning and Transformer experience, and reinforcement learning or fine-tuning background. A first-author paper in a top AI venue and strong Python and PyTorch skills are also required, with English essential for the role.

Skills

Python
PyTorch
DeepSpeed
Megatron
distributed training frameworks
reinforcement learning
RLHF
GRPO
PPO
SFT
LoRA
MoE
self-supervised learning
Transformer architectures
LLM fine-tuning
agentic reinforcement learning
multi-agent RL
tool-use and browsing agents
benchmark design
scientific writing
research publication
GPU cluster experimentation

Keywords

Reinforcement Learning
LLM
RLHF
GRPO
PPO
agentic AI
tool-using agents
LLM evaluation
PyTorch
DeepSpeed
Megatron
distributed training
Transformers
deep learning
Python
AI research
contract
Huawei

Seniority

Senior

Tools

PyTorch
DeepSpeed
Megatron
Python

Languages

English
French

Fields of study

Computer Science

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