Machine Learning Engineer (Content Ecology & Creator) -E-commerce Governance

TikTok Inc.; Seattle (WA); Hybrid; USD 154,000 - 301,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays

10 paid sick days

17 days Paid Personal Time

Job summary

TikTok Inc. is expanding its Governance & Experience Algorithm Team in Seattle, focusing on building a prosperous and fair global e-commerce ecosystem. You will work with LLM agents, RAG, GNN, and sequence modeling to govern creator activity, detect risks, and promote high-quality content.

Join a team that shapes policy-aware AI systems, balances creator fairness with platform health, and drives innovative solutions across Content Governance, Trust & Safety, and marketplace optimization.

Qualifications

• Bachelor's degree or above in computer science or related field.
• Proficient in Python/C++ with strong hands-on experience in PyTorch or TensorFlow.
• Deep expertise in NLP/LLM, Graph Neural Networks, Sequence Modeling, or ML.
• 1+ years of experience in Content Governance, Trust & Safety, Creator Ecology, or Advertising/Search/Recommendation.

Responsibilities

• Develop signal-driven creator profiling and quality scores.
• Build LLM + RAG governance systems with explainable reasoning.
• Create heterogeneous graphs to uncover relationships and detect bad actors.
• Apply multi-objective optimization and fairness algorithms to balance ecosystem metrics.

Skills

Python/C++

PyTorch

TensorFlow

NLP/LLM

Graph Neural Networks

Sequence Modeling

Content Governance

Education

Bachelor's degree or above in computer science

Tools

PyTorch

TensorFlow

Job description

Responsibilities

Global E-Commerce | Governance & Experience Algorithm Team

About the TeamBuilding a Prosperous, Trusted, and Fair Global E-Commerce Ecosystem

We are the Governance & Experience Algorithm Team, the AI guardians ensuring the long-term health of TikTok Shop's global platform.

As our international business expands, our mission goes beyond traditional risk control. We are dedicated to constructing a prosperous, trusted content ecosystem and maintaining a fair, healthy environment for creators.

We leverage LLM agents, RAG, GNN, and Sequence Modeling to solve complex governance challenges. We don't just block bad actors; we shape the rules of the game to ensure that creativity is rewarded, fairness is upheld, and the ecosystem thrives.

Our Core Mission:

• Trust & Quality: Ensuring users trust what they see, establishing a standard where "Good Content = Good Business."
• Creator Governance: Managing the full lifecycle of creators by identifying malicious intent (e.g., piracy, content mills) while protecting high-potential authentic creators.
• Ecosystem Fairness: using AI to ensure fair traffic distribution and prevent monopolies by bad actors, fostering a diverse and sustainable creator community.

What You'll Do

1. Creator Governance & Quality Modeling

• Signal-Driven Creator Profiling: aggregated underlying multi-modal signals (e.g., static frames, low-aesthetic detection, piracy fingerprints) to build comprehensive Creator Quality Scores.
• Combat Low-Quality & Malicious Intent: Develop sequence-based models to detect and penalize creators engaging in "low-effort selling," "re-recording/piracy," and "matrix account spamming," effectively purging the ecosystem of noise.
• LLM & RAG Intelligent Governance: Build LLM + RAG systems that dynamic interpret complex governance policies. Develop agents that not only flag risky creators but provide explainable reasoning to guide creator education and improvement.

2. Graph Intelligence & Syndicate Detection

• Heterogeneous Graph Mining: Construct large-scale Heterogeneous Graphs (Creator-Product-Video-User) to uncover hidden relationships and organized bad actors (e.g., fake engagement rings, black-market account trading, sybil attacks).
• Cross-Domain Risk Propagation: Utilize graph algorithms to track how risk propagates across different scenarios (Content vs. Shelf) and markets, predicting where bad actors will migrate next.

3. Ecosystem Strategy, Fairness & Optimization

• Multi-Objective Optimization (MMoE/PLE): Develop advanced multi-task learning models to balance conflicting objectives-maximizing Ecosystem Prosperity and GMV while minimizing Governance Risk and User Complaints.
• Fairness Algorithms: Design traffic regulation strategies that prevent the "rich get richer" effect for low-quality diverse content, ensuring fair exposure for high-quality, original creators.

Qualifications

Minimum Qualifications:

• Bachelor's degree or above in computer science or related field
• Proficient in Python/C++ with strong hands-on experience in PyTorch or TensorFlow
• Deep expertise in at least one of the following areas: NLP/LLM (Agents/Tuning), Graph Neural Networks (GNN), Sequence Modeling, or Machine Learning
• 1+ years of experience in Content Governance, Trust & Safety, Creator Ecology, or Advertising/Search/Recommendation

Preferred Qualifications

• Cutting-Edge Application: Experience with RAG, DPO/RLHF, or Multi-Modal Representation Learning in a production environment is highly preferred
• You view problems through an ecosystem lens-caring about Health, Fairness, and Diversity, not just binary classification metrics (Precision/Recall)
• Ability to translate abstract business goals (e.g., "Improve Creator Fairness") into concrete mathematical definitions and model targets
• Strong communication skills to articulate algorithmic strategies to Policy, Operations, and Product teams
• You enjoy the "cat and mouse" game of outsmarting evolving bad actor techniques

Job Information

The base salary range for this position in the selected city is $153900 - $300960 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems;
3. Exercising sound judgment.

Responsibilities

Global E-Commerce | Governance & Experience Algorithm Team

About the TeamBuilding a Prosperous, Trusted, and Fair Global E-Commerce Ecosystem

We are the Governance & Experience Algorithm Team, the AI guardians ensuring the long-term health of TikTok Shop's global platform.

As our international business expands, our mission goes beyond traditional risk control. We are dedicated to constructing a prosperous, trusted content ecosystem and maintaining a fair, healthy environment for creators.

We leverage LLM agents, RAG, GNN, and Sequence Modeling to solve complex governance challenges. We don't just block bad actors; we shape the rules of the game to ensure that creativity is rewarded, fairness is upheld, and the ecosystem thrives.

Our Core Mission:

• Trust & Quality: Ensuring users trust what they see, establishing a standard where "Good Content = Good Business."
• Creator Governance: Managing the full lifecycle of creators by identifying malicious intent (e.g., piracy, content mills) while protecting high-potential authentic creators.
• Ecosystem Fairness: using AI to ensure fair traffic distribution and prevent monopolies by bad actors, fostering a diverse and sustainable creator community.

What You'll Do

1. Creator Governance & Quality Modeling

• Signal-Driven Creator Profiling: aggregated underlying multi-modal signals (e.g., static frames, low-aesthetic detection, piracy fingerprints) to build comprehensive Creator Quality Scores.
• Combat Low-Quality & Malicious Intent: Develop sequence-based models to detect and penalize creators engaging in "low-effort selling," "re-recording/piracy," and "matrix account spamming," effectively purging the ecosystem of noise.
• LLM & RAG Intelligent Governance: Build LLM + RAG systems that dynamic interpret complex governance policies. Develop agents that not only flag risky creators but provide explainable reasoning to guide creator education and improvement.

2. Graph Intelligence & Syndicate Detection

• Heterogeneous Graph Mining: Construct large-scale Heterogeneous Graphs (Creator-Product-Video-User) to uncover hidden relationships and organized bad actors (e.g., fake engagement rings, black-market account trading, sybil attacks).
• Cross-Domain Risk Propagation: Utilize graph algorithms to track how risk propagates across different scenarios (Content vs. Shelf) and markets, predicting where bad actors will migrate next.

3. Ecosystem Strategy, Fairness & Optimization

• Multi-Objective Optimization (MMoE/PLE): Develop advanced multi-task learning models to balance conflicting objectives-maximizing Ecosystem Prosperity and GMV while minimizing Governance Risk and User Complaints.
• Fairness Algorithms: Design traffic regulation strategies that prevent the "rich get richer" effect for low-quality diverse content, ensuring fair exposure for high-quality, original creators.