Intern Researcher - AI Computing System

About the role

Aiming at key industry AI application scenarios such as large model training and inference, this internship focuses on advancing performance, efficiency, and usability of AI systems on the Ascend platform. The work involves low‑precision training, multimodal optimization, reinforcement learning, and training‑resource optimization to address system bottlenecks and deliver next‑generation AI capabilities.

Key responsibilities

• Design and develop optimization solutions for AI training and inference systems, emphasizing FP8 optimization, RL‑driven training agents, and multimodal reinforcement learning.
• Combine AI algorithm requirements with system‑level architectural optimization in computing, I/O, scheduling, and precision control to improve performance.
• Build stable, efficient AI training clusters using dynamic configuration and precision control to ensure scalability and reliability.
• Develop software frameworks, operator libraries, acceleration libraries, and system‑level optimizations for NPU platforms to accelerate large‑model AI training.

Required profile

• Ph.D. or Master’s student in Computer Science, Computer Engineering, or related fields such as AI, robotics, or electronics.
• Familiarity with large‑model structures (e.g., Deepseek, Llama) and basic experience in LLM, MoE, or multimodal training and inference optimization.
• Understanding of AI accelerator hardware (GPU/NPU) and experience optimizing AI systems across software and hardware cores.

Required skills

• Python
• C
• C++