Content Quality and Evaluation Project Intern (AI Data Service Operations) - 2026 Start
Aug 28, 2026 Ho Chi Minh City, Vietnam
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 5,001 - 10,000 employees
- Industry: Entertainment & Gaming
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 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.
As an Eco Content Quality and Evaluation intern, you'll sit at the intersection of model performance and ecosystem governance. You'll act as the gatekeeper for AI training and evaluation data - defining quality standards, unifying judgment criteria, and safeguarding data trustworthiness and consistency, so that every data point holds up under the scrutiny of both models and the business.
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.
Key Responsibilities
1. Help design and iterate on data quality standards, judgment rules, and acceptance criteria for LLM training and evaluation scenarios;
2. Review and adjudicate data outputs against quality standards, identify issues, and close the loop on corrections to ensure accuracy and consistency;
3. Dig into complex, ambiguous, and edge cases; drive discussion and turn conclusions into reusable judgment rules and knowledge assets;
4. Track quality metrics, root-cause issues, and drive improvements in production processes and execution;
5. Partner cross-functionally with production, product, algorithm, and policy teams to align on quality standards and connect the "standard → production → acceptance → feedback" loop.
Qualifications
Minimum Qualifications
- Currently pursuing an Undergraduate in data, statistics, computer science, law, linguistics or social sciences preferred.
- Internship, academic project, campus organization, or full-time experience in data quality, quality assurance, content moderation, compliance, content operations, editorial review, or AI data / annotation is a strong plus;
- Excellent English proficiency (listening, speaking, reading, writing) with accurate comprehension of English video and text content; IELTS 7.5 / TOEFL 105 or equivalent
- Proficient in Excel and common data tools; hands-on experience with mainstream LLM products (ChatGPT, Claude, Gemini, etc.) and a basic understanding of AI capability boundaries preferred;
- Strong logical thinking with structured problem-decomposition, abstraction, and articulation skills; detail-oriented, with your own point of view on "what makes good data."
- Demonstrated ability to interpret and apply complex guidelines or policies in writing-focused workflows, and some experience evaluating qualitative content or using data to improve processes. Internship, research, and project experience all count.
Preferred Qualifications
- Additional experience with content policy, operational guideline development, or enforcement workflows.
- A self-starter mindset, solution-oriented thinking, and the ability to manage multiple priorities in a fast-paced, collaborative environment.
- Familiarity with machine-executable logic, labeling frameworks, or test-set workflows.
- Experience collaborating with Policy, Product, Governance, Engineering, or Training teams through internships, projects, research, or full-time roles.
- Knowledge of scenario coverage, positive/negative balance, and dataset validation.
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 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.
As an Eco Content Quality and Evaluation intern, you'll sit at the intersection of model performance and ecosystem governance. You'll act as the gatekeeper for AI training and evaluation data - defining quality standards, unifying judgment criteria, and safeguarding data trustworthiness and consistency, so that every data point holds up under the scrutiny of both models and the business.
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.
Key Responsibilities
1. Help design and iterate on data quality standards, judgment rules, and acceptance criteria for LLM training and evaluation scenarios;
2. Review and adjudicate data outputs against quality standards, identify issues, and close the loop on corrections to ensure accuracy and consistency;
3. Dig into complex, ambiguous, and edge cases; drive discussion and turn conclusions into reusable judgment rules and knowledge assets;
4. Track quality metrics, root-cause issues, and drive improvements in production processes and execution;
5. Partner cross-functionally with production, product, algorithm, and policy teams to align on quality standards and connect the "standard → production → acceptance → feedback" loop.
Minimum Qualifications
- Currently pursuing an Undergraduate in data, statistics, computer science, law, linguistics or social sciences preferred.
- Internship, academic project, campus organization, or full-time experience in data quality, quality assurance, content moderation, compliance, content operations, editorial review, or AI data / annotation is a strong plus;
- Excellent English proficiency (listening, speaking, reading, writing) with accurate comprehension of English video and text content; IELTS 7.5 / TOEFL 105 or equivalent
- Proficient in Excel and common data tools; hands-on experience with mainstream LLM products (ChatGPT, Claude, Gemini, etc.) and a basic understanding of AI capability boundaries preferred;
- Strong logical thinking with structured problem-decomposition, abstraction, and articulation skills; detail-oriented, with your own point of view on "what makes good data."
- Demonstrated ability to interpret and apply complex guidelines or policies in writing-focused workflows, and some experience evaluating qualitative content or using data to improve processes. Internship, research, and project experience all count.
Preferred Qualifications
- Additional experience with content policy, operational guideline development, or enforcement workflows.
- A self-starter mindset, solution-oriented thinking, and the ability to manage multiple priorities in a fast-paced, collaborative environment.
- Familiarity with machine-executable logic, labeling frameworks, or test-set workflows.
- Experience collaborating with Policy, Product, Governance, Engineering, or Training teams through internships, projects, research, or full-time roles.
- Knowledge of scenario coverage, positive/negative balance, and dataset validation.
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 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.
As an Eco Content Quality and Evaluation intern, you'll sit at the intersection of model performance and ecosystem governance. You'll act as the gatekeeper for AI training and evaluation data - defining quality standards, unifying judgment criteria, and safeguarding data trustworthiness and consistency, so that every data point holds up under the scrutiny of both models and the business.
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.
Key Responsibilities
1. Help design and iterate on data quality standards, judgment rules, and acceptance criteria for LLM training and evaluation scenarios;
2. Review and adjudicate data outputs against quality standards, identify issues, and close the loop on corrections to ensure accuracy and consistency;
3. Dig into complex, ambiguous, and edge cases; drive discussion and turn conclusions into reusable judgment rules and knowledge assets;
4. Track quality metrics, root-cause issues, and drive improvements in production processes and execution;
5. Partner cross-functionally with production, product, algorithm, and policy teams to align on quality standards and connect the "standard → production → acceptance → feedback" loop.
Qualifications
Minimum Qualifications
- Currently pursuing an Undergraduate in data, statistics, computer science, law, linguistics or social sciences preferred.
- Internship, academic project, campus organization, or full-time experience in data quality, quality assurance, content moderation, compliance, content operations, editorial review, or AI data / annotation is a strong plus;
- Excellent English proficiency (listening, speaking, reading, writing) with accurate comprehension of English video and text content; IELTS 7.5 / TOEFL 105 or equivalent
- Proficient in Excel and common data tools; hands-on experience with mainstream LLM products (ChatGPT, Claude, Gemini, etc.) and a basic understanding of AI capability boundaries preferred;
- Strong logical thinking with structured problem-decomposition, abstraction, and articulation skills; detail-oriented, with your own point of view on "what makes good data."
- Demonstrated ability to interpret and apply complex guidelines or policies in writing-focused workflows, and some experience evaluating qualitative content or using data to improve processes. Internship, research, and project experience all count.
Preferred Qualifications
- Additional experience with content policy, operational guideline development, or enforcement workflows.
- A self-starter mindset, solution-oriented thinking, and the ability to manage multiple priorities in a fast-paced, collaborative environment.
- Familiarity with machine-executable logic, labeling frameworks, or test-set workflows.
- Experience collaborating with Policy, Product, Governance, Engineering, or Training teams through internships, projects, research, or full-time roles.
- Knowledge of scenario coverage, positive/negative balance, and dataset validation.
Responsibilities
ResponsibilitiesAbout 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 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.
As an Eco Content Quality and Evaluation intern, you'll sit at the intersection of model performance and ecosystem governance. You'll act as the gatekeeper for AI training and evaluation data - defining quality standards, unifying judgment criteria, and safeguarding data trustworthiness and consistency, so that every data point holds up under the scrutiny of both models and the business.
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.
Key Responsibilities
1. Help design and iterate on data quality standards, judgment rules, and acceptance criteria for LLM training and evaluation scenarios;
2. Review and adjudicate data outputs against quality standards, identify issues, and close the loop on corrections to ensure accuracy and consistency;
3. Dig into complex, ambiguous, and edge cases; drive discussion and turn conclusions into reusable judgment rules and knowledge assets;
4. Track quality metrics, root-cause issues, and drive improvements in production processes and execution;
5. Partner cross-functionally with production, product, algorithm, and policy teams to align on quality standards and connect the "standard → production → acceptance → feedback" loop.
Qualifications
QualificationsMinimum Qualifications
- Currently pursuing an Undergraduate in data, statistics, computer science, law, linguistics or social sciences preferred.
- Internship, academic project, campus organization, or full-time experience in data quality, quality assurance, content moderation, compliance, content operations, editorial review, or AI data / annotation is a strong plus;
- Excellent English proficiency (listening, speaking, reading, writing) with accurate comprehension of English video and text content; IELTS 7.5 / TOEFL 105 or equivalent
- Proficient in Excel and common data tools; hands-on experience with mainstream LLM products (ChatGPT, Claude, Gemini, etc.) and a basic understanding of AI capability boundaries preferred;
- Strong logical thinking with structured problem-decomposition, abstraction, and articulation skills; detail-oriented, with your own point of view on "what makes good data."
- Demonstrated ability to interpret and apply complex guidelines or policies in writing-focused workflows, and some experience evaluating qualitative content or using data to improve processes. Internship, research, and project experience all count.
Preferred Qualifications
- Additional experience with content policy, operational guideline development, or enforcement workflows.
- A self-starter mindset, solution-oriented thinking, and the ability to manage multiple priorities in a fast-paced, collaborative environment.
- Familiarity with machine-executable logic, labeling frameworks, or test-set workflows.
- Experience collaborating with Policy, Product, Governance, Engineering, or Training teams through internships, projects, research, or full-time roles.
- Knowledge of scenario coverage, positive/negative balance, and dataset validation.
Additional Information
- Job category: Operations
- Posted: Aug 28, 2026