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.
- AI Search & AI Bot Team: Specializes in LLM application algorithm R&D for TikTok Search, covering core scenarios including AI Search Q&A cards and TikTok's official AI Assistant. We leverage LLM ChatBot, Agent, and RAG technologies to deliver accurate, real-time search responses for users, driving business growth through deep integration of technical innovation and business scenarios.
- Multimodal Large Model Team: Builds industry-leading large model-based search technology, with deep expertise in NLP, CV, and multimodal technology domains. We explore cutting-edge technology applications in complex search scenarios, with technical achievements directly empowering TikTok Search functions to enhance user experience.
- Local Services Search Team: Optimizes Local Service search experience, helping users discover catering, hotel, leisure and other life services. We apply LLM for query understanding, CTR/CVR prediction, local-service ai search card and agentic local service search framework.
- Vertical Search Team: Build a full-stack search engine serving billions of global users, covering vertical scenarios such as photo-text search, user search, live search and music search. The scope also encompasses AI-powered search for standalone apps including Drama and Lemon8. We leverage LLM, NLP, and multimodal understanding technologies to iteratively enhance search quality and system performance.
- Search Ranking Team: By building a full stack search engine system based on advanced AI and machine learning models, we provide industry-leading search experience for billions of global users. We have cutting edge technologies in fields like LLM for Recommendation, Large Ranking Model, Sequence Modeling, Multi-modal Understanding, etc.
We are looking for talented individuals to join us for an internship. Our internship program offers students hands-on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.
Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.
Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.
Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.
Responsibilities
- Under the guidance of a mentor, participate in optimizing algorithm modules for local services search, such as query analysis, relevance, recall, coarse ranking, fine ranking, and blended ranking, and help improve user search experience and ecosystem growth
- Under the guidance of a mentor, contribute to improving TikTok core and vertical search (image-text, user, live, etc.) quality by supporting parts of the pipeline including query understanding, content understanding, and ranking
- Assist in developing and iterating search ranking algorithms by working on specific components that align product objectives with machine learning, AI, and NLP/CV techniques (e.g., features, models, evaluation, or offline/online analysis)
- Support the optimization of e-commerce search recommendation services and models by participating in module-level work (e.g., traffic analysis, candidate generation/ranking improvements, or experimentation), helping increase e-commerce search traffic and enhance user search and purchase cognition
- Contribute to recommendation system optimization by participating in parts of the end-to-end workflow from recall and coarse ranking to fine ranking, with exposure to hyperscale machine learning models and engineering practices
- Collaborate with product and infrastructure teams in daily development and iteration, helping deliver improvements through well-scoped tasks, experiments, or tooling under mentorship
Minimum Qualifications
- Currently pursuing a Undergraduate/ Master's in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
- Proficient in at least one of C++/Python/Java, with a solid foundation in algorithms and data structures
- Solid understanding of machine learning fundamentals; hands-on experience through coursework, lab projects, or internships is a plus
- Strong analytical, modeling, and problem-solving skills, with the ability to derive insights from complex data with guidance and iteration
Preferred Qualifications
- Experience (coursework, lab projects, competitions, or open-source exploration) in one or more domains: advertising, search engine, recommendation systems, NLP/CV
- Project experience related to e-commerce, search, recommendation, or advertising (e.g., class projects, lab research, hackathons, or personal projects)
- Good communication and teamwork skills, with the ability to collaborate effectively in a fast-paced environment
- Competition awards (e.g., ACM-ICPC) or strong project/research outputs (e.g., technical reports, demos, open-source contributions) are a plus
- Good product sense and user experience awareness, with interest in understanding product goals and translating them into measurable improvements