AI Data Strategy & Model Operations Specialist Graduate (AI Data Service Operations) - 2027 Start

About the Team Generative AI and large language models are reshaping the core capabilities of global content platforms — and high-quality training and evaluation data is one of the key factors that determines a model's ceiling.

We are the AI Data Service & Operations (ADSO) team behind TikTok's international products, responsible for producing multilingual, multimodal data assets across both safety and non-safety domains. The standards we set and the data we deliver power two critical fronts: TikTok's global content ecosystem governance strategy on one side, and the training and evaluation pipelines for AI / large language models on the other. In short, we produce the fuel that helps models truly understand content, communities, and users around the world.

Role Summary We are looking for an AI Data Strategy & Model Operations Specialist to help turn large model training and evaluation needs into high-quality, scalable data production plans. This role works closely with algorithm, product, business, quality, and production teams to define data requirements, coordinate delivery, improve standards, and solve bottlenecks across the AI data lifecycle.

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. 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 our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Key Responsibilities 1. Translate AI model training and evaluation needs from algorithm and business teams into clear annotation, production, and delivery tasks. 2. Coordinate capacity, workforce, timeline, and cost considerations across annotation and production teams to support stable delivery at scale. 3. Define and iterate data production standards, workflows, and quality inspection rules to improve consistency and output quality. 4. Track operational issues and delivery bottlenecks, identify root causes, and drive practical improvements in efficiency and quality. 5. Use SQL, Excel, dashboards, and AI tools to support data analysis, delivery monitoring, and operational decision-making. 6. Work with product, algorithm, business, and quality teams to align data standards, acceptance criteria, and feedback loops.