Search Safety PM Project Intern (TikTok Safety Product) - 2026 Start

About the Team The Safety Product team is at the forefront of building and optimizing content safety systems. With a focus on optimising and advancing content safety, we leverage advanced large language models to enhance review efficiency, risk control, and user trust. Working closely with business and technical stakeholders, we deliver scalable solutions that keep pace with rapid global growth. As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests. Applications are reviewed on a rolling basis, so we encourage you to apply early.

Responsibilities - Assist in the construction and daily maintenance of high-quality LLM training datasets and knowledge bases, guaranteeing overall data accuracy, diversity and contextual completeness to support ongoing model training initiatives. - Participate in the design and iteration of labeling systems and taxonomies, and generate large-scale, highly consistent labeled datasets to support supervised learning and reinforcement learning workflows for LLMs. - Independently generate, review and polish massive LLM training data in strict accordance with unified standards, business use cases and evaluation criteria to ensure standardized data output. - Draft, iterate and optimize targeted prompts for various model training and evaluation scenarios, and conduct in-depth analysis of model outputs to identify deficiencies, biases and failure patterns, so as to optimize prompt design and clarify data iteration requirements. - Fully leverage professional understanding of LLM capabilities and limitations to design targeted training data and prompt strategies, and accurately extract core intent and key information from multi-domain complex content to form standardized structured training inputs. - Conduct full-process quality inspection and consistency calibration on datasets, labels and prompt contents, continuously optimize data production standards and operational workflows based on model feedback and project iteration demands, and improve overall training data quality.