LLM/Multimodal AI Engineer Intern (TikTok Search) - 2027 Start

Responsibilities

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.

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
- Participate in the development of AI Search Agent components under mentor guidance, including planning, memory, tool-use, and evaluation modules for search-related scenarios
- Support LLM-based query understanding tasks, such as intent analysis, multi-turn conversation understanding, prompt optimization, and offline evaluation of model outputs
- Assist in experiments related to LLM adaptation for search scenarios, including data preparation, model fine-tuning, reinforcement learning feedback analysis, and metric tracking
- Contribute to multimodal search projects by supporting visual representation learning, video understanding, data processing, and model evaluation tasks
- Help optimize RAG and LLM pipelines, including retrieval data construction, ranking feature analysis, answer quality evaluation, and structured result presentation
- Work closely with mentors and cross-functional partners to prototype, validate, and iterate on algorithm modules, contributing to product and system improvements in TikTok Search

Qualifications

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 programming language such as C++ or Python, with a solid foundation in algorithms and data structures
- Basic understanding of machine learning and deep learning fundamentals, with coursework, projects, or research experience in NLP, CV, multimodal learning, recommendation, search, or related domains
- Familiarity with LLM fundamentals, such as prompt engineering, supervised fine-tuning, retrieval-augmented generation, or model evaluation
- Strong analytical and problem-solving skills, with curiosity and willingness to learn new technologies
- Able to commit to an internship and collaborate effectively in a fast-paced, cross-functional environment

Preferred Qualifications
- Experience with relevant lab research, coursework projects, or personal projects in LLM, Agent, RAG, multimodal learning, search, recommendation, or advertising systems
- Hands-on exploration of LLM/Agent-related technologies, such as RAG optimization, SFT/RLHF experiments, Agent framework development, planning or memory modules, or evaluation pipelines
- Experience participating in technical competitions, open-source projects, research prototypes, or other practical explorations in AI-related fields is a plus
- Familiarity with search, recommendation, information retrieval, ranking, or large-scale machine learning systems is a plus
- Strong learning ability, ownership, and communication skills, with enthusiasm for applying cutting-edge AI technologies to real-world product scenarios