Job Description

Team Introduction:

Our Search Team is responsible for building and owning our search engine which provides our users the best search experience. On the Search Team, you’ll have the opportunity to build a full-stack search engine system and combine information retrieval technology with modern machine learning methods from related fields such as NLP, Computer Vision, Multimodal, and Recommender Systems. We embrace a culture of self-direction, intellectual curiosity, openness, and problem-solving.

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Topic Content:

With the rapid advancement of foundation model technology, the field of AI-powered search is encountering new opportunities and challenges. Traditional search technologies have begun to reveal significant limitations when confronted with massive data volumes, multimodal information, and complex multi-turn user needs. It is therefore necessary to leverage foundation models to build next-generation AI search systems, enhancing the intelligence of search systems and optimizing user experience. Specific objectives include:

1. Explore the integration of foundation models with ranking algorithms to improve personalized ranking accuracy and user experience.

2. Explore end-to-end generative search models based on multimodal pre-training.

3. Explore LLM-based agent technology to improve user satisfaction under complex ambiguous queries and multi-turn search scenarios.

Topic Challenges:

1. Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.

2. Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.

3. Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.

Topic Value:

1. Technical value: Break through the bottlenecks of traditional search technology; build a next-generation AI search architecture driven by LLM agents; and address industry challenges including personalized ranking, ultra-large-scale retrieval and ranking, and understanding and fulfilling complex search needs.

2. Business value: Significantly improve search user experience and satisfaction, driving improvements in search LT and users' proactive search intent.