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

BYTEDANCE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

BYTEDANCE PTE. LTD. in Singapore is seeking a highly skilled researcher to advance next-generation AI search architectures by combining information retrieval with large language models, multimodal data, and machine learning.

You will tackle challenges in personalized ranking, ultra-large-scale retrieval, and understanding complex user queries. A PhD is required, with proficiency in C/C++ or Python and a track record of ML/DL/LLM research; publications are preferred.

Qualifications

• PhD in AI, CS, or related technical discipline.
• Strong programming skills in C/C++ or Python with solid ML/DL/LLM foundation.
• Excellent publication record preferred.

Responsibilities

• Explore integration of foundation models with ranking algorithms for improved personalized ranking.
• Develop end-to-end generative search models based on multimodal pre-training.
• Experiment with LLM-based agent technology to improve user satisfaction in complex queries and multi-turn scenarios.

Skills

C/C++

Python

ML/DL/LLM

Education

PhD in AI/CS/CE or related

Job description

Team Introduction

Our TikTok 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

• 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.

Topic Challenges

• 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.
• 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.
• 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

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

Minimum Qualifications

• Individuals who are completing or recently completed a PhD in AI, Computer Science, Computer Engineering, or a related technical discipline.
• Excellent coding skills and a solid foundation in Machine Learning/Deep Learning/LLM, proficient in C/C++ or Python

Preferred Qualifications

• Outstanding ability to define, analyze, and solve problems; candidates with publications at AI conferences such as AAAI, NeurIPS, ACL, etc., or in-depth research experience are preferred.
• Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.