Research Scientist Graduate (TikTok - Data - Search - Search Quality) - 2027 Start (PhD)

Our Search Engineering Team is responsible for building and owning TikTok's search engine, which can provide our users with the best search experience. In TikTok Search Team, you'll have the opportunity to build full-stack search engine systems and combine information retrieval technology with modern machine learning methods from related fields such as NLP, CV, and recommender systems. We embrace a culture of self-direction, intellectual curiosity, openness, and problem-solving. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. This full-time PhD Graduate role provides early-career researchers and engineers the opportunity to join one of our engineering teams where you will have the opportunity to: Participate in applied research and improvement of the search core algorithm, possible directions include: - Content understanding: Applying the industry's cutting-edge NLP and CV technology and leveraging LLM to match the most relevant videos for each search query, and continuously improve the relevance of TikTok search. - User Behavior Modeling: Solving the recommendation problem in search, let TikTok search increase the ability of personalization on the basis of "relevant", and understand users better. - Video understanding: Comprehensively using NLP, CV, as well as LLM for better video understanding from the perspective of the video itself and social network, improve authority, credibility, and usefulness of search results. - Search research and evaluation: Developing new retrieval, ranking, relevance evaluation, query understanding, multimodal search, or LLM application methods and translating research ideas into scalable product improvements. Qualifications Minimum Qualification(s) - Individuals currently pursuing or recently completed a PhD degree in Computer Science, Electrical Engineering, Machine Learning, Artificial Intelligence, Information Retrieval, Natural Language Processing, Data Mining, Statistics, or a related technical field. - Must have graduated with an applicable degree before onboarding. - Research or practical experience in one or more areas: search, information retrieval, ranking, recommendation systems, natural language processing, multimodal learning, large language models, data mining, or machine learning systems. Preferred Qualification(s) - Publications, preprints, patents, open-source contributions, or research projects in search, recommendation, NLP, information retrieval, machine learning, or related areas. - Experience with large-scale retrieval, ranking, model training, evaluation, online experimentation, distributed systems, or production ML systems. - Familiarity with relevance evaluation, query understanding, semantic retrieval, vector search, LLM applications, or multimodal search. - Ability to define ambiguous technical problems, compare solution paths, and communicate trade-offs clearly. - Interest in applying research to improve search quality, user experience, and product impact at global scale.