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

TikTok Inc.; San Jose (CA); On-site; USD 39,000 - 55,000; Part time

Job summary

TikTok Inc. invites PhD students to join the TikTok Search team for an internship in 2026, collaborating with top engineers and researchers on a world-class search experience across TikTok's global ecosystem.

You will work across the full stack of search—from content understanding and retrieval to ranking and user experience optimization—focusing on generative and multimodal models to address large-scale challenges.

Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, AI, or related field.
• Solid foundation in machine learning, deep learning, or information retrieval.
• Familiarity with LLMs, NLP, search/recommendation, or multimodal learning.

Responsibilities

• Support the development of AI search systems using large models (LLMs).
• Assist in improving ranking, personalization, and relevance in search.
• Contribute to generative and multimodal search models (video, text, image).
• Explore LLM-based approaches for complex and multi-turn queries.
• Help optimize retrieval and ranking performance at scale.
• Collaborate with mentors and cross-functional teams to prototype and evaluate solutions.

Skills

Python

Problem solving

Fast learning

Education

PhD candidate in Computer Science/ML

Tools

PyTorch

TensorFlow

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.

Key focus areas include:

• - 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.

Project Value:

• - 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 us for an internship in 2026. 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).

Responsibilities

• - Support the development of AI search systems using large models (LLMs).
• - Assist in improving ranking, personalization, and relevance in search.
• - Contribute to generative and multimodal search models (video, text, image).
• - Explore LLM‑based approaches for complex and multi‑turn queries.
• - Help optimize retrieval and ranking performance at scale.
• - Collaborate with mentors and cross‑functional teams to prototype and evaluate solutions.

Minimum Qualifications

• - Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
• - Solid foundation in machine learning, deep learning, or information retrieval.
• - Familiarity with at least one of the following: LLMs, NLP, search/recommendation, or multimodal learning.
• - Proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow).
• - Strong problem‑solving skills and willingness to learn in a fast‑paced environment.

Preferred Qualifications

• - Research experience demonstrated through projects, publications, or open‑source contributions.
• - Experience with large‑scale data, model training, or system implementation.
• - Exposure to search, recommendation, or ads systems.
• - Experience with multimodal models or generative AI applications.
• - Interest in LLMs, agent‑based systems, or end‑to‑end AI applications.

Previous internship or research experience in industry or labs.

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.

Key focus areas include:

• - 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.

Project Value:

• - 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 us for an internship in 2026. 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).

Responsibilities

• - Support the development of AI search systems using large models (LLMs).
• - Assist in improving ranking, personalization, and relevance in search.
• - Contribute to generative and multimodal search models (video, text, image).
• - Explore LLM‑based approaches for complex and multi‑turn queries.
• - Help optimize retrieval and ranking performance at scale.
• - Collaborate with mentors and cross‑functional teams to prototype and evaluate solutions.

Minimum Qualifications

• - Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
• - Solid foundation in machine learning, deep learning, or information retrieval.
• - Familiarity with at least one of the following: LLMs, NLP, search/recommendation, or multimodal learning.
• - Proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow).
• - Strong problem‑solving skills and willingness to learn in a fast‑paced environment.

Preferred Qualifications

• - Research experience demonstrated through projects, publications, or open‑source contributions.
• - Experience with large‑scale data, model training, or system implementation.
• - Exposure to search, recommendation, or ads systems.
• - Experience with multimodal models or generative AI applications.
• - Interest in LLMs, agent‑based systems, or end‑to‑end AI applications.

Previous internship or research experience in industry or labs.