AI Research Engineer - GPU Scheduling & Resource Management - Intern

About Huawei

Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. With integrated solutions across four key domains – telecom networks, IT, smart devices, and cloud services – we are committed to bringing digital to every person, home and organization for a fully connected, intelligent world.

At Huawei, innovation focuses on customer needs. We invest heavily in basic research, concentrating on technological breakthroughs that drive the world forward. We have more than 180,000 employees, and we operate in more than 170 countries and regions.

About The Irc

Huawei Ireland Research Centre (IRC) mission is to position Huawei as a recognized technology leader and a global provider of information and communications technology (ICT) solutions. To achieve this we are building an industry-recognized multi-discipline Research Centre of experts with focus on medium-term to long-term issues. The IRC will work closely with an open innovative ecosystem with Huawei customers to address real-world issues. The IRC will also engage with key European universities to build a basic research capability to support Huawei technical projects.

About The Role

We are looking for an AI Research Engineer Intern to work on Huawei Cloud Inference Serving Team. Join us to push the boundaries of what's possible in Distributed AI inference performance and reliability. In this role, you won't be just writing code; you will be also solving some of the most complex and exciting challenges in distributed Inference serving. Your work will directly impact the latency, efficiency, security and stability of our cloud-based AI services.

As part of our GPU scheduling and resource management focus, you'll tackle key challenges around intelligent resource allocation, performance optimization under variable workloads, and building systems that efficiently orchestrate heterogeneous computing resources while meeting strict performance and reliability requirements.

Responsibilities

• Design and implement scheduling algorithms that efficiently allocate GPU/NPU resources across multiple concurrent inference requests
• Contribute to performance optimization efforts through profiling, benchmarking, and system-level tuning
• Build monitoring and analytics tools to measure resource utilization patterns and identify performance bottlenecks
• Develop and enhance resource management components that maximize hardware efficiency while maintaining SLA guarantees
• Collaborate with AI researchers and ML engineers to understand workload characteristics and translate requirements into system designs
• Write well-tested, maintainable code and participate in code reviews with the team

Requirements

• Currently pursuing BS/MS in Computer Science, Computer Engineering, or related field
• Strong programming skills in Python, C++, or similar systems languages
• Solid understanding of computer systems fundamentals including operating systems, concurrency, and distributed systems
• Experience with performance analysis, profiling tools, and optimization techniques
• Knowledge of GPU computing concepts and parallel programming models
• Understanding of machine learning workload characteristics and resource requirements

Preferred

• Experience with CUDA programming or GPU compute frameworks
• Knowledge of containerization (Docker) and orchestration tools (Kubernetes)
• Familiarity with machine learning frameworks (PyTorch, TensorFlow) and inference optimization
• Previous experience with high-performance computing or distributed systems projects
• Understanding of scheduling algorithms and resource management in distributed environments

What You'Ll Gain

This internship offers hands-on experience with cutting-edge AI infrastructure at scale, working alongside senior engineers who will mentor you through complex technical challenges. You'll gain deep insights into GPU resource management, performance optimization, and distributed systems design while contributing to production systems that serve real users. The experience will provide valuable skills in systems engineering, performance analysis, and large-scale infrastructure that are highly sought after in the industry.

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