Senior Technical VP - AI & Efficient Deep Learning

About the team

The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications.

One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.

• Conduct in-depth analysis of technological trends in the AI field, lead the core competitiveness planning and technology roadmap in AI computing, and ensure industry-leading product performance and commercial success in future AI inference & training applications.
• Deeply understand the chip and system architecture, and implement system-level innovation through co-design of hardware/software/algorithms. Build the core technical framework of AI computing systems, driving industry-leading AI system solutions.

• Degree in Computer Science, Mathematics/Statistics, Engineering, or related fields, with solid mathematical knowledge. Deep understanding of AI theory and proficiency in SOTA large-scale models (such as Llama and Deep Seek) and fundamental AI algorithm principles.
• Knowledge about AI chip architecture and interconnection technologies. Proficiency in hardware/software co-design and optimization. In-depth understanding of key technologies in AI systems, including high-performance operator libraries, collective communication libraries, distributed acceleration, parallelism strategies, and efficient dispatching systems.
• Experience in computing efficiency optimization of large models, including optimization of inference latency and throughput, and MFU enhancement. Familiarity with distributed training and inference frameworks such as Megatron, vLLM, and SGLang.
• More than 5 years of hands-on experience in AI fields such as CV, LLM, Multi Modal, Reasoning, and Agent. Experience in leading large-scale inference/training system research projects or product design & development, demonstrating outstanding commercial results and significant technical influence.
• Publication record in top-tier computer architecture/computer system/AI conferences is preferred.