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

Responsibilities

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 Model Evaluation & Quality Strategy Specialist to help define what high-quality data means for large model training and evaluation. This role focuses on quality standards, judgment rules, complex case assessment, issue correction, and feedback loops that improve AI model evaluation outcomes across markets.

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. Participate in defining and continuously improving data quality standards, judgment rules, and acceptance criteria for large model training and evaluation scenarios.
2. Review data outputs against quality standards, identify issues, drive correction loops, and ensure accuracy and consistency.
3. Assess complex, ambiguous, and boundary cases, and turn decisions into reusable judgment rules and knowledge assets.
4. Track quality performance, analyze root causes, and recommend improvements to production-side workflows and execution.
5. Work with production, product, algorithm, strategy, and quality teams to align standards across the full chain from standard-setting to production, acceptance, and feedback.