Machine Learning Engineer, Monetization Data Alignment
May 26, 2026 Singapore
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
As TikTok's monetization ecosystem continues to grow across ads, e-commerce, short video, and live streaming, the demand for accurate, scalable, and efficient labeled data and content understanding systems is increasing rapidly. Our team is responsible for improving how commercial content is understood, labeled, structured, and operationalized at scale. We combine advanced machine learning with strong engineering execution to support data production, model innovation, and business impact.
About the role
We are looking for an experienced Machine Learning Engineer to join the Monetization Data Alignment team. This role sits at the intersection of AI labeling, content understanding, multimodal large models, and agentic systems. You will work on building the next generation of scalable intelligence capabilities that power TikTok monetization use cases, with a strong focus on high-quality data, multimodal reasoning, and production-grade ML systems.
In this role, you will contribute to both research and production: from exploring LLM/MLLM, Agent, and reinforcement learning techniques, to delivering robust systems for labeling automation, multimodal understanding, rule retrieval, agent development, and interpretable decision-making.
Responsibilities
- Design and develop machine learning solutions for AI labeling and content understanding in TikTok monetization scenarios, supporting ads, short video, and other monetization products.
- Apply and improve LLM/MLLM, NLP, CV, and multimodal learning techniques to enhance fine-grained understanding across text, image, audio, video, and live content.
- Build algorithms and systems for labeling automation, intent recognition, taxonomy/tag generation, rule retrieval, risk detection, and quality evaluation, improving both model accuracy and operational efficiency.
- Explore and productionize Agent and RL based approaches for complex decision-making workflows, including multi-step reasoning, tool use, and adaptive content analysis.
- Develop interpretable solutions such as CoT-style reasoning, explanation generation, and traceable decision logic to improve trustworthiness and operational usability of model outputs.
Qualifications
Minimum Qualification(s)
- Bachelor's degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics, or related fields.
- 3+ years of experience in machine learning, applied AI, or related algorithm engineering roles, with hands-on experience in taking models or AI systems from experimentation to production.
- Solid foundation in machine learning and deep learning, with strong understanding of one or more of the following areas: LLM/MLLM, NLP, CV, multimodal learning, representation learning, or intelligent agents.
- Hands-on experience with large model training, fine-tuning, evaluation, inference, or deployment; familiarity with techniques such as prompting, supervised fine-tuning, retrieval augmented generation, chain-of-thought style reasoning, model alignment, or agent workflows.
- Ability to break down ambiguous business problems, design practical ML solutions, and collaborate effectively with cross-functional stakeholders in a fast-paced environment.
- Strong communication skills in English, and the ability to work effectively with global teams across research, product, engineering, and operations.
Preferred Qualification(s)
- Experience in AI labeling, content understanding, ads understanding, multimodal tagging, taxonomy systems, or data quality systems is highly preferred.
- Familiarity with Agent, reinforcement learning, RLHF/RLAIF, autonomous decision systems, or multi-step reasoning pipelines.
- Track record of strong research or innovation output, such as publications, patents, open-source contributions, or high-impact internal technical projects.
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, WWW, AAAI, IJCAI, or related areas are a strong plus.
As TikTok's monetization ecosystem continues to grow across ads, e-commerce, short video, and live streaming, the demand for accurate, scalable, and efficient labeled data and content understanding systems is increasing rapidly. Our team is responsible for improving how commercial content is understood, labeled, structured, and operationalized at scale. We combine advanced machine learning with strong engineering execution to support data production, model innovation, and business impact.
About the role
We are looking for an experienced Machine Learning Engineer to join the Monetization Data Alignment team. This role sits at the intersection of AI labeling, content understanding, multimodal large models, and agentic systems. You will work on building the next generation of scalable intelligence capabilities that power TikTok monetization use cases, with a strong focus on high-quality data, multimodal reasoning, and production-grade ML systems.
In this role, you will contribute to both research and production: from exploring LLM/MLLM, Agent, and reinforcement learning techniques, to delivering robust systems for labeling automation, multimodal understanding, rule retrieval, agent development, and interpretable decision-making.
Responsibilities
- Design and develop machine learning solutions for AI labeling and content understanding in TikTok monetization scenarios, supporting ads, short video, and other monetization products.
- Apply and improve LLM/MLLM, NLP, CV, and multimodal learning techniques to enhance fine-grained understanding across text, image, audio, video, and live content.
- Build algorithms and systems for labeling automation, intent recognition, taxonomy/tag generation, rule retrieval, risk detection, and quality evaluation, improving both model accuracy and operational efficiency.
- Explore and productionize Agent and RL based approaches for complex decision-making workflows, including multi-step reasoning, tool use, and adaptive content analysis.
- Develop interpretable solutions such as CoT-style reasoning, explanation generation, and traceable decision logic to improve trustworthiness and operational usability of model outputs.
Minimum Qualification(s)
- Bachelor's degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics, or related fields.
- 3+ years of experience in machine learning, applied AI, or related algorithm engineering roles, with hands-on experience in taking models or AI systems from experimentation to production.
- Solid foundation in machine learning and deep learning, with strong understanding of one or more of the following areas: LLM/MLLM, NLP, CV, multimodal learning, representation learning, or intelligent agents.
- Hands-on experience with large model training, fine-tuning, evaluation, inference, or deployment; familiarity with techniques such as prompting, supervised fine-tuning, retrieval augmented generation, chain-of-thought style reasoning, model alignment, or agent workflows.
- Ability to break down ambiguous business problems, design practical ML solutions, and collaborate effectively with cross-functional stakeholders in a fast-paced environment.
- Strong communication skills in English, and the ability to work effectively with global teams across research, product, engineering, and operations.
Preferred Qualification(s)
- Experience in AI labeling, content understanding, ads understanding, multimodal tagging, taxonomy systems, or data quality systems is highly preferred.
- Familiarity with Agent, reinforcement learning, RLHF/RLAIF, autonomous decision systems, or multi-step reasoning pipelines.
- Track record of strong research or innovation output, such as publications, patents, open-source contributions, or high-impact internal technical projects.
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, WWW, AAAI, IJCAI, or related areas are a strong plus.
As TikTok's monetization ecosystem continues to grow across ads, e-commerce, short video, and live streaming, the demand for accurate, scalable, and efficient labeled data and content understanding systems is increasing rapidly. Our team is responsible for improving how commercial content is understood, labeled, structured, and operationalized at scale. We combine advanced machine learning with strong engineering execution to support data production, model innovation, and business impact.
About the role
We are looking for an experienced Machine Learning Engineer to join the Monetization Data Alignment team. This role sits at the intersection of AI labeling, content understanding, multimodal large models, and agentic systems. You will work on building the next generation of scalable intelligence capabilities that power TikTok monetization use cases, with a strong focus on high-quality data, multimodal reasoning, and production-grade ML systems.
In this role, you will contribute to both research and production: from exploring LLM/MLLM, Agent, and reinforcement learning techniques, to delivering robust systems for labeling automation, multimodal understanding, rule retrieval, agent development, and interpretable decision-making.
Responsibilities
- Design and develop machine learning solutions for AI labeling and content understanding in TikTok monetization scenarios, supporting ads, short video, and other monetization products.
- Apply and improve LLM/MLLM, NLP, CV, and multimodal learning techniques to enhance fine-grained understanding across text, image, audio, video, and live content.
- Build algorithms and systems for labeling automation, intent recognition, taxonomy/tag generation, rule retrieval, risk detection, and quality evaluation, improving both model accuracy and operational efficiency.
- Explore and productionize Agent and RL based approaches for complex decision-making workflows, including multi-step reasoning, tool use, and adaptive content analysis.
- Develop interpretable solutions such as CoT-style reasoning, explanation generation, and traceable decision logic to improve trustworthiness and operational usability of model outputs.
Qualifications
Minimum Qualification(s)
- Bachelor's degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics, or related fields.
- 3+ years of experience in machine learning, applied AI, or related algorithm engineering roles, with hands-on experience in taking models or AI systems from experimentation to production.
- Solid foundation in machine learning and deep learning, with strong understanding of one or more of the following areas: LLM/MLLM, NLP, CV, multimodal learning, representation learning, or intelligent agents.
- Hands-on experience with large model training, fine-tuning, evaluation, inference, or deployment; familiarity with techniques such as prompting, supervised fine-tuning, retrieval augmented generation, chain-of-thought style reasoning, model alignment, or agent workflows.
- Ability to break down ambiguous business problems, design practical ML solutions, and collaborate effectively with cross-functional stakeholders in a fast-paced environment.
- Strong communication skills in English, and the ability to work effectively with global teams across research, product, engineering, and operations.
Preferred Qualification(s)
- Experience in AI labeling, content understanding, ads understanding, multimodal tagging, taxonomy systems, or data quality systems is highly preferred.
- Familiarity with Agent, reinforcement learning, RLHF/RLAIF, autonomous decision systems, or multi-step reasoning pipelines.
- Track record of strong research or innovation output, such as publications, patents, open-source contributions, or high-impact internal technical projects.
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, WWW, AAAI, IJCAI, or related areas are a strong plus.
Responsibilities
ResponsibilitiesAs TikTok's monetization ecosystem continues to grow across ads, e-commerce, short video, and live streaming, the demand for accurate, scalable, and efficient labeled data and content understanding systems is increasing rapidly. Our team is responsible for improving how commercial content is understood, labeled, structured, and operationalized at scale. We combine advanced machine learning with strong engineering execution to support data production, model innovation, and business impact.
About the role
We are looking for an experienced Machine Learning Engineer to join the Monetization Data Alignment team. This role sits at the intersection of AI labeling, content understanding, multimodal large models, and agentic systems. You will work on building the next generation of scalable intelligence capabilities that power TikTok monetization use cases, with a strong focus on high-quality data, multimodal reasoning, and production-grade ML systems.
In this role, you will contribute to both research and production: from exploring LLM/MLLM, Agent, and reinforcement learning techniques, to delivering robust systems for labeling automation, multimodal understanding, rule retrieval, agent development, and interpretable decision-making.
Responsibilities
- Design and develop machine learning solutions for AI labeling and content understanding in TikTok monetization scenarios, supporting ads, short video, and other monetization products.
- Apply and improve LLM/MLLM, NLP, CV, and multimodal learning techniques to enhance fine-grained understanding across text, image, audio, video, and live content.
- Build algorithms and systems for labeling automation, intent recognition, taxonomy/tag generation, rule retrieval, risk detection, and quality evaluation, improving both model accuracy and operational efficiency.
- Explore and productionize Agent and RL based approaches for complex decision-making workflows, including multi-step reasoning, tool use, and adaptive content analysis.
- Develop interpretable solutions such as CoT-style reasoning, explanation generation, and traceable decision logic to improve trustworthiness and operational usability of model outputs.
Qualifications
QualificationsMinimum Qualification(s)
- Bachelor's degree or above in Computer Science, Artificial Intelligence, Mathematics, Statistics, or related fields.
- 3+ years of experience in machine learning, applied AI, or related algorithm engineering roles, with hands-on experience in taking models or AI systems from experimentation to production.
- Solid foundation in machine learning and deep learning, with strong understanding of one or more of the following areas: LLM/MLLM, NLP, CV, multimodal learning, representation learning, or intelligent agents.
- Hands-on experience with large model training, fine-tuning, evaluation, inference, or deployment; familiarity with techniques such as prompting, supervised fine-tuning, retrieval augmented generation, chain-of-thought style reasoning, model alignment, or agent workflows.
- Ability to break down ambiguous business problems, design practical ML solutions, and collaborate effectively with cross-functional stakeholders in a fast-paced environment.
- Strong communication skills in English, and the ability to work effectively with global teams across research, product, engineering, and operations.
Preferred Qualification(s)
- Experience in AI labeling, content understanding, ads understanding, multimodal tagging, taxonomy systems, or data quality systems is highly preferred.
- Familiarity with Agent, reinforcement learning, RLHF/RLAIF, autonomous decision systems, or multi-step reasoning pipelines.
- Track record of strong research or innovation output, such as publications, patents, open-source contributions, or high-impact internal technical projects.
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, KDD, WWW, AAAI, IJCAI, or related areas are a strong plus.
Additional Information
- Posted: May 26, 2026