Principal AI Researcher, Dublin Pike

About Huawei R&D Ireland Huawei’s Irish R&D Centre in Dublin is one of the company’s most advanced global research hubs, focused on pioneering next-generation AI, telecommunications, and autonomous systems. Huawei spends over $20 billion annually on R&D globally, making it one of the world’s largest R&D investors. The Dublin lab specifically focuses on applying advanced AI techniques— including Agentic AI, Foundation Models, and Reinforcement Learning — to solve frontier problems in intelligent network management.

Role Overview As a Principal AI Researcher focused on Autonomous AI Agents, you will lead original research into Foundation Model architectures and Agentic AI systems specifically designed for telecommunication network applications. This is a senior, high-visibility position within Huawei’s global AI strategy. Your research will be published in top-tier venues and directly translated into production-grade systems that manage and optimize global telecom networks at unprecedented scale.

Key Responsibilities

Lead original research on Foundation Models, Agentic Reinforcement Learning, and multi-agent coordination systems

Design and experiment with novel autonomous AI agent architectures for network planning, optimization, and self-healing

Publish research at top AI conferences (NeurIPS, ICML, ICLR, IEEE ComSoc)

Develop reasoning and planning modules that allow agents to autonomously manage complex network states

Collaborate with international R&D teams across China, Germany, Canada, and Ireland on joint research agendas

Translate research prototypes into production-ready AI systems deployed in live network environments

Mentor junior researchers and guide the team’s technical roadmapEngage with academic partnerships and represent Huawei at AI research forums

Required Skills&Qualifications

PhD in AI, Machine Learning, or Computer Science (strongly preferred)

7+ years of experience in AI/ML research with a strong publication record

Deep expertise in Reinforcement Learning, LLM architectures, and multi-agent systems

Proficiency in Python, PyTorch, and distributed training frameworks

Experience with reasoning systems, agent planning, and autonomous decision-making

Background in telecommunications or network systems is a major advantage

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