Research Scientist Intern - End-to-End Generative Search Technology Based on Pre-Trained Foundation Models - Global Tech Research Program - 2027 Start (PhD)

Responsibilities

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 us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

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.