Senior Product Manager (AI Solutions), Trust & Safety
Jun 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
About the Team
We are the TnS-Platform Product team, sitting in TnS-RPR(Risk Prevention and Response) , aiming to provide product solutions with tooling support for RPR business and XFN to inspire, preserve, and sustain trust with society stakeholders.
Key Responsibilities
- Own end-to-end product development for selected high-impact AI problem spaces within Risk Prevention & Response (RPR) business under Trust and Safety, such as proactive risk discovery, signal investigation, risk assessment, response and containment, or prevention.
- Develop a deep understanding of complex risk governance workflows, identify high-value opportunities for AI, and independently translate ambiguous business problems into clear product solutions and execution plans.
- Design AI-native workflows and Human + AI collaboration models, defining where AI can operate autonomously, where human expertise and judgment are required, and how agents, experts, tools, and systems should work together effectively.
- Drive AI products from problem discovery and 0→1 experimentation through production launch, adoption, and iteration, partnering closely with Operations, Engineering, ML/Data Science, Policy, QA, and other product teams.
- Design scalable AI product capabilities, including agent workflows, tool and data integration, context management, human-in-the-loop mechanisms, and intelligent decision support, rather than solving individual use cases through one-off solutions.
- Define success metrics and evaluation frameworks for owned AI capabilities, assessing quality, reliability, efficiency, adoption, and business impact, and use real-world results to drive continuous improvement.
- Ensure AI solutions are appropriate for high-risk and sensitive environments, with appropriate consideration for explainability, evidence traceability, auditability, human oversight, and clearly defined risk boundaries.
- Contribute to the broader RPR AI roadmap by bringing insights from owned problem spaces, identifying reusable capabilities, and exploring emerging AI opportunities.
Qualifications
Minimum Qualifications
- Bachelor's degree or above, with 3+ years of product management experience. Experience with platform, workflow, or technically complex products is preferred.
- Hands-on experience with LLMs, AI Agents, intelligent automation, or AI-powered products, with demonstrated ability to translate AI capabilities into real-world product solutions.
- Demonstrated ability to independently own a complex product or problem space end to end, from problem definition and solution design through execution, launch, measurement, and iteration.
- Strong ability to understand and decompose complex workflows, identify high-leverage problems, and turn ambiguity into structured product decisions.
- Strong product judgment and analytical thinking, with the ability to balance AI capabilities, user needs, operational requirements, business impact, and risk.
- Proven ability to collaborate effectively with Engineering, ML/Data Science, Operations, and other cross-functional stakeholders and drive complex initiatives without relying on formal authority.
- High learning agility, curiosity, and self-motivation. Comfortable operating in ambiguous, fast-changing environments and continuously learning as AI technologies evolve.
Preferred Qualifications:
- Experience in Trust & Safety, content safety, risk governance, fraud/abuse prevention, cybersecurity, integrity, or other complex risk domains.
- Experience taking an AI product, Agent, workflow automation capability, or intelligent operations tool from 0→1 into production.
- Experience designing Human-in-the-Loop systems, AI evaluation frameworks, or AI products where reliability and auditability are critical.
- Technical fluency with concepts such as LLMs, Agents, tool use, RAG/context retrieval, model evaluation, and AI workflow orchestration; engineering or ML background is a plus.
- Strong data sense and experience using metrics, experimentation, and evaluation results to drive product decisions.
About the Team
We are the TnS-Platform Product team, sitting in TnS-RPR(Risk Prevention and Response) , aiming to provide product solutions with tooling support for RPR business and XFN to inspire, preserve, and sustain trust with society stakeholders.
Key Responsibilities
- Own end-to-end product development for selected high-impact AI problem spaces within Risk Prevention & Response (RPR) business under Trust and Safety, such as proactive risk discovery, signal investigation, risk assessment, response and containment, or prevention.
- Develop a deep understanding of complex risk governance workflows, identify high-value opportunities for AI, and independently translate ambiguous business problems into clear product solutions and execution plans.
- Design AI-native workflows and Human + AI collaboration models, defining where AI can operate autonomously, where human expertise and judgment are required, and how agents, experts, tools, and systems should work together effectively.
- Drive AI products from problem discovery and 0→1 experimentation through production launch, adoption, and iteration, partnering closely with Operations, Engineering, ML/Data Science, Policy, QA, and other product teams.
- Design scalable AI product capabilities, including agent workflows, tool and data integration, context management, human-in-the-loop mechanisms, and intelligent decision support, rather than solving individual use cases through one-off solutions.
- Define success metrics and evaluation frameworks for owned AI capabilities, assessing quality, reliability, efficiency, adoption, and business impact, and use real-world results to drive continuous improvement.
- Ensure AI solutions are appropriate for high-risk and sensitive environments, with appropriate consideration for explainability, evidence traceability, auditability, human oversight, and clearly defined risk boundaries.
- Contribute to the broader RPR AI roadmap by bringing insights from owned problem spaces, identifying reusable capabilities, and exploring emerging AI opportunities.
Minimum Qualifications
- Bachelor's degree or above, with 3+ years of product management experience. Experience with platform, workflow, or technically complex products is preferred.
- Hands-on experience with LLMs, AI Agents, intelligent automation, or AI-powered products, with demonstrated ability to translate AI capabilities into real-world product solutions.
- Demonstrated ability to independently own a complex product or problem space end to end, from problem definition and solution design through execution, launch, measurement, and iteration.
- Strong ability to understand and decompose complex workflows, identify high-leverage problems, and turn ambiguity into structured product decisions.
- Strong product judgment and analytical thinking, with the ability to balance AI capabilities, user needs, operational requirements, business impact, and risk.
- Proven ability to collaborate effectively with Engineering, ML/Data Science, Operations, and other cross-functional stakeholders and drive complex initiatives without relying on formal authority.
- High learning agility, curiosity, and self-motivation. Comfortable operating in ambiguous, fast-changing environments and continuously learning as AI technologies evolve.
Preferred Qualifications:
- Experience in Trust & Safety, content safety, risk governance, fraud/abuse prevention, cybersecurity, integrity, or other complex risk domains.
- Experience taking an AI product, Agent, workflow automation capability, or intelligent operations tool from 0→1 into production.
- Experience designing Human-in-the-Loop systems, AI evaluation frameworks, or AI products where reliability and auditability are critical.
- Technical fluency with concepts such as LLMs, Agents, tool use, RAG/context retrieval, model evaluation, and AI workflow orchestration; engineering or ML background is a plus.
- Strong data sense and experience using metrics, experimentation, and evaluation results to drive product decisions.
About the Team
We are the TnS-Platform Product team, sitting in TnS-RPR(Risk Prevention and Response) , aiming to provide product solutions with tooling support for RPR business and XFN to inspire, preserve, and sustain trust with society stakeholders.
Key Responsibilities
- Own end-to-end product development for selected high-impact AI problem spaces within Risk Prevention & Response (RPR) business under Trust and Safety, such as proactive risk discovery, signal investigation, risk assessment, response and containment, or prevention.
- Develop a deep understanding of complex risk governance workflows, identify high-value opportunities for AI, and independently translate ambiguous business problems into clear product solutions and execution plans.
- Design AI-native workflows and Human + AI collaboration models, defining where AI can operate autonomously, where human expertise and judgment are required, and how agents, experts, tools, and systems should work together effectively.
- Drive AI products from problem discovery and 0→1 experimentation through production launch, adoption, and iteration, partnering closely with Operations, Engineering, ML/Data Science, Policy, QA, and other product teams.
- Design scalable AI product capabilities, including agent workflows, tool and data integration, context management, human-in-the-loop mechanisms, and intelligent decision support, rather than solving individual use cases through one-off solutions.
- Define success metrics and evaluation frameworks for owned AI capabilities, assessing quality, reliability, efficiency, adoption, and business impact, and use real-world results to drive continuous improvement.
- Ensure AI solutions are appropriate for high-risk and sensitive environments, with appropriate consideration for explainability, evidence traceability, auditability, human oversight, and clearly defined risk boundaries.
- Contribute to the broader RPR AI roadmap by bringing insights from owned problem spaces, identifying reusable capabilities, and exploring emerging AI opportunities.
Qualifications
Minimum Qualifications
- Bachelor's degree or above, with 3+ years of product management experience. Experience with platform, workflow, or technically complex products is preferred.
- Hands-on experience with LLMs, AI Agents, intelligent automation, or AI-powered products, with demonstrated ability to translate AI capabilities into real-world product solutions.
- Demonstrated ability to independently own a complex product or problem space end to end, from problem definition and solution design through execution, launch, measurement, and iteration.
- Strong ability to understand and decompose complex workflows, identify high-leverage problems, and turn ambiguity into structured product decisions.
- Strong product judgment and analytical thinking, with the ability to balance AI capabilities, user needs, operational requirements, business impact, and risk.
- Proven ability to collaborate effectively with Engineering, ML/Data Science, Operations, and other cross-functional stakeholders and drive complex initiatives without relying on formal authority.
- High learning agility, curiosity, and self-motivation. Comfortable operating in ambiguous, fast-changing environments and continuously learning as AI technologies evolve.
Preferred Qualifications:
- Experience in Trust & Safety, content safety, risk governance, fraud/abuse prevention, cybersecurity, integrity, or other complex risk domains.
- Experience taking an AI product, Agent, workflow automation capability, or intelligent operations tool from 0→1 into production.
- Experience designing Human-in-the-Loop systems, AI evaluation frameworks, or AI products where reliability and auditability are critical.
- Technical fluency with concepts such as LLMs, Agents, tool use, RAG/context retrieval, model evaluation, and AI workflow orchestration; engineering or ML background is a plus.
- Strong data sense and experience using metrics, experimentation, and evaluation results to drive product decisions.
Responsibilities
ResponsibilitiesAbout the Team
We are the TnS-Platform Product team, sitting in TnS-RPR(Risk Prevention and Response) , aiming to provide product solutions with tooling support for RPR business and XFN to inspire, preserve, and sustain trust with society stakeholders.
Key Responsibilities
- Own end-to-end product development for selected high-impact AI problem spaces within Risk Prevention & Response (RPR) business under Trust and Safety, such as proactive risk discovery, signal investigation, risk assessment, response and containment, or prevention.
- Develop a deep understanding of complex risk governance workflows, identify high-value opportunities for AI, and independently translate ambiguous business problems into clear product solutions and execution plans.
- Design AI-native workflows and Human + AI collaboration models, defining where AI can operate autonomously, where human expertise and judgment are required, and how agents, experts, tools, and systems should work together effectively.
- Drive AI products from problem discovery and 0→1 experimentation through production launch, adoption, and iteration, partnering closely with Operations, Engineering, ML/Data Science, Policy, QA, and other product teams.
- Design scalable AI product capabilities, including agent workflows, tool and data integration, context management, human-in-the-loop mechanisms, and intelligent decision support, rather than solving individual use cases through one-off solutions.
- Define success metrics and evaluation frameworks for owned AI capabilities, assessing quality, reliability, efficiency, adoption, and business impact, and use real-world results to drive continuous improvement.
- Ensure AI solutions are appropriate for high-risk and sensitive environments, with appropriate consideration for explainability, evidence traceability, auditability, human oversight, and clearly defined risk boundaries.
- Contribute to the broader RPR AI roadmap by bringing insights from owned problem spaces, identifying reusable capabilities, and exploring emerging AI opportunities.
Qualifications
QualificationsMinimum Qualifications
- Bachelor's degree or above, with 3+ years of product management experience. Experience with platform, workflow, or technically complex products is preferred.
- Hands-on experience with LLMs, AI Agents, intelligent automation, or AI-powered products, with demonstrated ability to translate AI capabilities into real-world product solutions.
- Demonstrated ability to independently own a complex product or problem space end to end, from problem definition and solution design through execution, launch, measurement, and iteration.
- Strong ability to understand and decompose complex workflows, identify high-leverage problems, and turn ambiguity into structured product decisions.
- Strong product judgment and analytical thinking, with the ability to balance AI capabilities, user needs, operational requirements, business impact, and risk.
- Proven ability to collaborate effectively with Engineering, ML/Data Science, Operations, and other cross-functional stakeholders and drive complex initiatives without relying on formal authority.
- High learning agility, curiosity, and self-motivation. Comfortable operating in ambiguous, fast-changing environments and continuously learning as AI technologies evolve.
Preferred Qualifications:
- Experience in Trust & Safety, content safety, risk governance, fraud/abuse prevention, cybersecurity, integrity, or other complex risk domains.
- Experience taking an AI product, Agent, workflow automation capability, or intelligent operations tool from 0→1 into production.
- Experience designing Human-in-the-Loop systems, AI evaluation frameworks, or AI products where reliability and auditability are critical.
- Technical fluency with concepts such as LLMs, Agents, tool use, RAG/context retrieval, model evaluation, and AI workflow orchestration; engineering or ML background is a plus.
- Strong data sense and experience using metrics, experimentation, and evaluation results to drive product decisions.
Additional Information
- Posted: Jun 26, 2026