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

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.

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 (https://www.openeuler.org/en/projects/yuanrong/) and vLLM-omni (https://github.com/vllm-project/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.

• 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.