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

About TikTok

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.

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

• Explore the integration of foundation models with ranking algorithms to improve personalized ranking accuracy and user experience.
• Explore end-to-end generative search models based on multimodal pre-training.
• Explore LLM-based agent technology to improve user satisfaction under complex ambiguous queries and multi-turn search scenarios.

Requirements

• Currently pursuing PhD in Computer Science, AI, Mathematics, or a related technical discipline, with a strong foundation in data structures, algorithms, and mathematical modeling.

Preferred

• AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning.
• Priority will be given to candidates with publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions.
• Excellent programming abilities in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs).
• Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.

Benefits

• 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

• 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 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.