Research Scientist - TikTok Search (Generative AI) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

TikTok Inc.; San Jose (CA); On-site; USD 218,000 - 480,000; Full time

Job summary

TikTok Inc. in California is looking for a campus-hire researcher to join the Search team. You will work with engineers and researchers to advance AI-native search across TikTok's multimodal ecosystem, from content understanding to ranking and user experience optimization.

You will develop scalable AI search systems using large models, improve personalization and relevance, and collaborate to ship research into production. A PhD or current PhD student with strong ML/NLP experience is expected.

Qualifications

• PhD in Computer Science, Computer Engineering, or related technical discipline.
• Solid foundation in ML, DL, or IR.
• Experience in LLMs, search/recommendation, ranking, NLP, or multimodal learning.
• Strong Python programming and ML framework familiarity (PyTorch, TensorFlow).
• Demonstrated research ability via publications, projects, or internships.
• Strong problem-solving and collaborative skills.

Responsibilities

• Develop next-generation AI search systems using large models (LLMs).
• Improve ranking, personalization, and relevance in search.
• Build generative and multimodal search models (video, text, image).
• Explore LLM-based agents for complex and multi-turn queries.
• Optimize large-scale retrieval and ranking performance.
• Collaborate cross-functionally to ship research into production.

Skills

Python

PyTorch

TensorFlow

Machine Learning

Deep Learning

Information Retrieval

LLMs

Search/Recommender Systems

NLP

Multimodal Learning

Publications/Research

Problem Solving

Collaboration

Education

PhD in Computer Science/Engineering or related

Job description

Responsibilities

About the team
The TikTok Search team is responsible for delivering a world-class search experience across TikTok's global ecosystem. Our mission is to connect users with the most relevant and high-quality content through advanced technologies in search ranking, recommendation, and multimodal understanding. As a campus hire, you will collaborate with top engineers and researchers to solve challenging problems at scale, working across the full stack of search-from content understanding and retrieval to ranking and user experience optimization.

Project Overview, Challenges & Value
With the rapid evolution of pre-trained large models, TikTok Search is advancing toward a new paradigm of AI-native search. Traditional search systems face increasing limitations when handling massive-scale data, multimodal content (video, text, image), and complex user intents. This project focuses on building a next-generation generative search system powered by large models to significantly enhance search intelligence and user experience.

• Integrating large models with ranking systems to improve personalization and relevance.
• Developing end-to-end generative search models based on multimodal pretraining.
• Exploring large model-driven agent frameworks to better support complex, ambiguous, and multi-turn search scenarios.

Key challenges include:
Personalized ranking: Fully leveraging multimodal signals beyond traditional model capacity.
Ultra-large-scale retrieval and ranking: Scaling search systems to efficiently handle hundreds of billions of candidates.
Complex query understanding: Accurately interpreting long, ambiguous, and multi-turn queries to improve user satisfaction.

• Technical Value: Drive innovation in large model-powered search architecture and enable agent-based AI search systems.
• Business Value: Significantly improve search relevance and user satisfaction, strengthening TikTok's search engagement and long-term growth.

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.

Responsibilities

• Develop next-generation AI search systems using large models (LLMs).
• Improve ranking, personalization, and relevance in search.
• Build generative and multimodal search models (video, text, image).
• Explore LLM-based agents for complex and multi-turn queries.
• Optimize large-scale retrieval and ranking performance.
• Collaborate cross-functionally to ship research into production.

Qualifications

Minimum Qualifications

• Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Solid foundation in machine learning, deep learning, or information retrieval.
• Experience in at least one of the following areas: LLMs, search/recommendation systems, ranking, NLP, or multimodal learning.
• Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow).
• Demonstrated research ability through publications, projects, or internships.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Track record of research contributions through publications, open-source projects, or impactful research work.
• Hands-on experience with large-scale systems, distributed training, or real-world ML deployment.
• Experience with search, recommendation, or ads ranking systems in industry or research.
• Experience with multimodal models (video, image, text) or generative AI applications.
• Familiarity with LLM fine-tuning, alignment (e.g., RLHF), or agent-based systems.
• Strong interest in building end-to-end AI systems from modeling to production.
• Internship or research experience at leading technology companies or research organizations.
• Modeling experience in one or more of the following areas: Ads, Search engine, Recommender System, NLP/CV.
• Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and practical experience in areas such as single-modal/multi-modal LLM application and deployment.
• Strong publications record in accredited conferences or journals (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, NAACL, CVPR, ICCV and ECCV).

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $218400 - $480000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.