Senior Principal Researcher & Technical Leader – Agentic RL for Distributed Computing in Markham

Huawei Canada has an immediate permanent opening for a Senior Principal Engineer.

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.

Join Huawei’s Distributed Computing Lab – where we’re redefining AI innovation in Canada. We are a distributed computing team dedicated to building scalable, high-performance systems and robust tools for the global open-source community. Our work focuses on advancing infrastructure for AI and data-intensive workloads, with strong emphasis on production-grade reliability and efficiency.

We are the creators of openYuanrong and vLLM-omni, helping shape the ecosystem for large-scale LLM serving and multi-modal inference. Operating at the intersection of distributed systems, AI infrastructure, and high-performance computing, we tackle challenges such as large-scale data movement, heterogeneous resource scheduling, and efficient multi-agent execution, delivering impactful, widely adopted open technologies.

About the job: As a Senior Principal leader at Huawei Canada Research Center, you will be the primary technical authority and strategic visionary for our Distributed AI Infrastructure. This is a legacy-free leadership role where you will move beyond current Python-heavy stack limitations to define a high-performance C++ native substrate. Your mission is to architect the "chassis" for the next era of intelligence: Agentic Reinforcement Learning (RL) and Multi-Agent Systems (MAS). We believe Agentic RL will define the next generation of AI systems, where models are not just predictors, but decision-making entities that interact, collaborate, and evolve.

• Lead technical innovation and exploration of AI infrastructure distributed systems for reinforcement learning and multi-agent systems.
• Drive multi-tier research and multi-scenario applications to build core technical competitiveness and support commercial success.
• Understand corporate/product line strategies and industry trends.
• Continuously identify business issues and core challenges, absorb the latest research from academia and industry, and solve the pain points and difficult problems of AI distributed systems.
• Act as a regional ecosystem builder, connecting academic resources in the North American AI distributed systems field, promoting academic cooperation, and building academic influence.

About the ideal candidate:

• Lead and define the architectural evolution of Agentic RL and MARL for cooperative, competitive, and mixed-agent environments, including CTDE, decentralized learning, and hierarchical systems.
• Lead the construction of simulation and training substrates for ultra-large-scale agent systems, establishing global standards for self-play, population-based training (PBT), and curriculum learning.
• Drive the optimization of agent learning performance on distributed clusters, resolving critical challenges in sample efficiency, credit assignment, and communication learning at scale.
• Decide on frontier research pathways for multi-agent intelligence, including communication protocols, game-theoretic learning dynamics, and meta-learning application prototypes.
• Direct the translation of cutting-edge Agentic AI and MARL research into production-grade distributed systems, ensuring robust deployment in complex simulated or real-world environments.
• Establish industry-leading benchmarking standards and evaluation frameworks for agent coordination, robustness, scalability, and safety.
• Bridge research, infrastructure, and product teams to lead the deployment and application of large-scale agentic learning systems in real-world business scenarios.
• Champion the team’s technical leadership in global academia and industry through high-impact publications, patents, and open-source contributions.
• Continuously track and evaluate technical advancements in Agentic AI and large-scale distributed systems to inform long-term corporate technology strategy.

PROFESSIONAL ATTRIBUTE:

• 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 at top-tier venues (e.g., NeurIPS, ICML, OSDI, SOSP), open-source contributions, patents, or production ML systems in reinforcement learning, multi-agent learning, or large-scale AI systems.