AI Computing Architecture Researcher

On behalf of Huawei, a world-renowned information and communication technology company, we are seeking passionate and talented individuals to join our team as AI Computing Architecture Researcher.

Key Responsibilities:

• Design and optimize AI computing architectures and platforms for LLM, AIGC, distributed AI, and intelligent agent workloads. Develop software components that interface with hardware accelerators such as GPUs, NPUs, and specialized AI chips.
• Improve performance and efficiency of distributed computing, parallel processing, and GPU/NPU acceleration.
• Collaborate with AI researchers to enhance platform performance for complex AI and agent-based applications and also to resolve system-level performance issues across architecture and platform components.
• Design and implement tools and frameworks for deployment, monitoring, scaling, and orchestration of AI workloads and agent systems.
• Integrate new AI models, agent controllers, and algorithms into the computing architecture and platform while ensuring scalability, fault tolerance, and efficiency.
• Conduct profiling, benchmarking, and performance optimization to achieve high throughput and low latency.
• Contribute to research on agent capabilities, including skill abstraction, composition, learning, and deployment.
• Stay updated on advancements in AI hardware, system architecture, and agent technologies, and continuously improve platform capabilities.

Required Skills and Qualifications:

• Master’s or PhD in Computer Science, Electrical Engineering, or related fields (PhD preferred), with strong experience in high-performance AI systems or computing platforms.
• Proficiency in C/C++, Python, AI-DSL with a focus on low-level programming for high-performance systems.
• In-depth knowledge of AI model optimization techniques such as quantization, model graph pruning, and model parameter compression and sparsity algorithm.
• In-depth knowledge of parallel programming, distributed systems, and multi-agent coordination strategies.
• Experience with GPU/NPU programming (e.g., CUDA, Triton, cuTile or similar DSLs).
• Familiarity with AI/machine learning frameworks such as PyTorch, TensorFlow, or MXNet.
• Understanding of AI model deployment, orchestration, and optimization on large-scale platforms, including agent skill deployment and runtime management.
• Experience with containerization (Docker, Kubernetes),LLM deployment platforms (SGLang, vLLM, HuggingFace), and cloud infrastructure (AWS, GCP, Azure).
• Strong problem-solving skills and the ability to optimize software performance for both traditional AI and agent-based workloads.