TikTok

About the Team
We are part of Global Monetization Product & Technology – Business Integrity. Our imission is to protect user trust and platform integrity by governing risks and improving experience across the ads ecosystem—ultimately enabling sustainable conversion efficiency for commercial content on TikTok.

About the Role
We are looking for an experienced, data-driven Senior Product Manager to build and scale AI-powered governance products, with After Conversion Experience (ACE) as the primary business scenario.
ACE focuses on risks and experience issues after a user completes an ads conversion (e.g., app install, form submission, purchase) and enters the advertiser’s post-conversion journey. This role will lead product strategy and execution to identify, explain, and mitigate user harm and integrity risks beyond the initial click—leveraging ML/LLMs, large-scale signals, and automation.

What You Will Build
- AI-native ACE governance system spanning: risk detection → decisioning → enforcement → feedback learning
- Risk taxonomy & policy framework that translates real-world ACE harms into scalable, machine-actionable definitions
- Multi-signal risk detection using creative/content, advertiser/website attributes, user feedback, conversion signals, and behavioral patterns
- LLM/AI productization for content understanding, risk summarization and policy violation diagnosis;
- Automated enforcement (ads rejection, traffic deboost, account suspension etc.)
- Measurement & evaluation to continuously improve precision/recall;

Key Responsibilities
- Define the vision, strategy, and roadmap for AI-driven ACE governance
- Partner with Engineering, Data Science, and Operations to deliver scalable systems end-to-end
- Establish robust offline + online evaluation and experimentation to drive iteration
- Own key metrics (e.g. Risk leak rate, false positives, user negative feedback rate etc.)
- Drive cross-functional alignment across ads platforms, integrity, legal/compliance (as needed), and global stakeholders

About The Team
Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively. The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.

We are looking for data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals. You will be deploying your engineering, data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.

Responsibilities
- Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
- Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.
- Risk Identification & Mitigation: Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
- Measurement Metrics & Statistical Validation: Define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
- Communication & Collaboration: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals. Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
- Data Analytics Services: Partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
- Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
- Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
- AI-Driven Automation and Insights: Leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale. Develop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.
- Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and company products through continuous education. Provide data training to empower the audit team to derive insights.

About The Team
Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively. The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.

We are looking for data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals. You will be deploying your engineering, data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.

Responsibilities
- Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
- Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.
- Risk Identification & Mitigation: Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
- Measurement Metrics & Statistical Validation: Define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
- Communication & Collaboration: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals. Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
- Data Analytics Services: Partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
- Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
- Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
- AI-Driven Automation and Insights: Leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale. Develop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.
- Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and company products through continuous education. Provide data training to empower the audit team to derive insights.

About The Team
Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively. The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.

We are looking for data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals. You will be deploying your engineering, data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.

Responsibilities
- Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
- Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.
- Risk Identification & Mitigation: Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
- Measurement Metrics & Statistical Validation: Define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
- Communication & Collaboration: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals. Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
- Data Analytics Services: Partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
- Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
- Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
- AI-Driven Automation and Insights: Leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale. Develop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.
- Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and company products through continuous education. Provide data training to empower the audit team to derive insights.

The Platform Responsibility team is at the forefront of building and optimizing content safety systems. With a focus on optimising and advancing content safety, we leverage advanced large language models to enhance review efficiency, risk control, and user trust. Working closely with business and technical stakeholders, we deliver scalable solutions that keep pace with rapid global growth.

- Own and evolve the GenAI / LLM safety evaluation framework across multimodal use cases, including text-to-text, text-to-image, and text-to-video, with a focus on measuring model performance across core safety and risk dimensions.
- Design and scale online safety evaluation systems, introducing new evaluation methodologies and driving automation and large-scale deployment to enable continuous model and product iteration.
- Build and maintain safety benchmarks by creating high-quality evaluation datasets and adversarial test cases, ensuring benchmark distributions closely reflect real-world user behavior and risk scenarios.
- Establish and continuously improve root cause analysis (RCA) processes, including standardized workflows for issue detection, attribution, and postmortem reviews, and distill reusable risk patterns and actionable optimization insights.
- Partner cross-functionally with Model, Algorithm, Product, Data, and Safety teams to translate key safety findings into optimization roadmaps and ensure closed-loop validation of improvements.