Agentic RL Researcher – Distributed Computing

Huawei Canada; Markham; On-site; CAD 106,000 - 156,000; Full time

Job summary

A leading technology company in Markham is seeking a Researcher to design and develop advanced reinforcement learning algorithms and scalable systems. Candidates should have an MS or PhD and expertise in RL, programming skills, and a proven track record in research or projects. The total target annual compensation for this role ranges from $106,000 to $156,000. Join us to contribute to groundbreaking projects in a collaborative environment.

Qualifications

• Strong expertise in multi-agent reinforcement learning in non-stationary environments.
• Experience in scalable RL training infrastructure.
• Demonstrated impact through research contributions.

Responsibilities

• Design and develop Agentic Reinforcement Learning and Multi-Agent Reinforcement Learning algorithms.
• Build scalable simulation and training platforms for agent systems.
• Collaborate with teams to deploy scalable agent systems.

Skills

Reinforcement learning algorithms

Multi-Agent Systems

Programming in Python

Programming in C++

Optimization methods

Education

MS or PhD in Computer Science or related field

Job description

Company

Huawei Canada has an immediate permanent opening for a Researcher.

About the team

The Distributed Data Storage and Management Lab leads research in distributed data systems, aiming to develop next-generation cloud serverless products that encompass core infrastructure and databases. This lab addresses various data challenges, including cloud-native disaggregated databases, pay-by-query user models, and optimizing low-level data transfers via RDMA. Teams within this lab create advanced cloud serverless data infrastructure and implement cutting-edge networking technologies for Huawei's global AI infrastructure.

About the job

• Design and develop advanced Agentic Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) algorithms for cooperative, competitive, and mixed-agent environments, including CTDE, decentralized learning, and hierarchical agent systems.
• Build scalable simulation and training platforms for large-scale agent systems, supporting self-play, population-based training, curriculum learning, and emergent behavior analysis.
• Optimize multi-agent learning performance on distributed compute clusters, improving sample efficiency, credit assignment, agent coordination, communication learning, and training stability.
• Research and prototype new approaches for multi-agent intelligence, including communication protocols, credit assignment, game-theoretic learning dynamics, meta-learning, and adaptive agent populations.
• Translate cutting-edge research in agentic AI and MARL into production-ready systems for real-world or high-fidelity simulated environments.
• Develop benchmarking frameworks and evaluation metrics for agent coordination, robustness, scalability, and safety.
• Collaborate with research, infrastructure, and product teams to deploy scalable agentic learning systems in real-world applications.
• Contribute to technical leadership and innovation through publications, patents, open-source contributions, and conference presentations.

total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience, and demonstrated expertise.

Job requirements

• MS or PhD in Computer Science, Electrical Engineering, or a related field, with a focus on Reinforcement Learning, Multi-Agent Systems, Agentic AI, or Distributed AI.
• Strong expertise in reinforcement learning algorithms, particularly in multi-agent settings (e.g., policy gradients, value-based methods, CTDE, credit assignment, and coordination in non-stationary environments).
• Solid foundations in optimization, probability, and game theory, with the ability to design and analyze complex learning systems.
• Experience building scalable RL training infrastructure, including distributed rollouts, large-scale simulation, and experiment pipelines.
• Strong programming skills in Python and/or C++, with experience developing high-performance or distributed ML systems.
• Demonstrated impact through research publications, open-source contributions, patents, or production ML systems in reinforcement learning, multi-agent learning, or large-scale AI systems.

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.