As business scenarios become increasingly complex, search, advertising, and recommendation are facing significant challenges. While large models can accurately capture user preferences and enhance personalization as well as content quality, they also impose stringent requirements on real-time performance, stability, and scalability. This introduces substantial technical challenges in areas such as distributed training, inference acceleration, heterogeneous hardware utilization, and multimodal data processing.
At the same time, the rapid growth in model scale and the proliferation of multimodal data have made it difficult for existing infrastructure to meet the demands of data processing efficiency and resource utilization. Our Team focuses on system and engineering innovations to overcome key technical bottlenecks and build efficient, stable, and scalable large-model solutions, providing a robust technical foundation for search, advertising, and recommendation scenarios.
- Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses.
- Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.