Senior Principal Researcher & Technical Leader - Agentic RL for Distributed Computing

Senior Principal Engineer

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. The 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

• 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.

Job requirements

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.