Large Model Algorithm Engineer Intern (Data Model) - 2026 Start (PHD)

Team Introduction The Data Model team focuses on the application and implementation of large models in the field of data intelligence, providing horizontal support for large-model solutions across multiple data products of the company. The team is committed to keeping up with cutting-edge advancements in large models, designing end-to-end algorithm optimization and evaluation solutions by integrating domain-specific model optimization strategies, and achieving intelligent upgrades of data products along with improved application effectiveness. We encourage agile innovation and dedicate ourselves to the continuous exploration and research of data intelligence topics. We are looking for talented individuals to join us for an internship in 2026*. PhD Internships at TikTok 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. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Successful candidates must be able to commit to at least 3 months long internship period. Responsibilities: - Data enhancement algorithm optimization: Design a data-centric algorithm framework for data reasoning tasks, optimize the Reasoning Data synthesis pipeline, and improve the logical consistency, domain adaptability, and diversity of training data. - Innovative integration of data and reasoning LLM: Address the Over/Under-Thinking issues in chain-of-thought generation, and explore technical challenges in applying LLMs to complex data analysis and development tasks. - Application and implementation of reasoning LLM: Track the technical evolution of open-source SOTA large models, practice domain adaptation optimization in the post-training phase, verify application value, achieve continuous iteration of effectiveness based on evaluation systems, and generate technical patents.