Senior Research Scientist, TikTok - Trust and Safety

Responsibilities

Our Trust and Safety RD team is fast-growing and responsible for building machine learning models and systems to identify and defend internet abuse and fraud on our platform. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to detect and improve the tremendous amount of data generated on the platform. With the continuous efforts of our team, TikTok can provide the best user experience and bring joy to everyone in the world.

We are looking for people like you with solid experience in designing and deploying state-of-the-art models in the combination of NLP and CV-related areas. This position will work with a team of excellent research scientists and machine learning engineers who can take initiative, design and develop advanced machine learning solutions, and deploy them directly to TikTok's global platform.

Responsibilities - What You'II Do
1. Lead the design, training, and deployment of foundation models, including LLM/VLM, to support a broad range of content safety tasks across modalities. Build general-purpose foundation models with centralized compute and scalable architecture, aimed at improving risk detection, compliance understanding, and moderation automation.
2. Tackle challenges in multilingual, multimodal, and low-resource scenarios by enhancing models' zero-shot and few-shot generalization across diverse safety domains.
3. Design and maintain a multimodal safety annotation framework, supporting high-quality training data and evaluation signals for both image and video understanding tasks. Explore and implement RLHF strategies to fine-tune model alignment with evolving safety policies and user intent.
4. Collaborate closely with infra, data, and platform teams to optimize large-scale training pipelines, improve model serving efficiency, and leverage centralized GPU resources effectively.
5. Partner with product, policy, and recommendation teams to integrate safety-oriented models into real-world moderation workflows, and continuously optimize for performance and interpretability.

Qualifications

Minimum Qualifications:
1. Solid experience in traditional machine learning, with over 2 years of research or development experience in large models; passion for technology, and ability to dive deep into coding and debugging.
2. Strong problem-solving skills in the face of complex challenges, with a proven track record of technical breakthroughs and business impact; clear logic and sharp insight.
3. Highly responsible, self-driven, with excellent learning and communication abilities; strong interest in staying up-to-date with advances in multimodal large models.
4. A strong team player, open to exploring new technologies and driving continuous technical evolution.

Preferred Qualifications:
1. Publications in top conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV) or experience in competitive AI challenges.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $218400 - $387600 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; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

Our Trust and Safety RD team is fast-growing and responsible for building machine learning models and systems to identify and defend internet abuse and fraud on our platform. Our mission is to protect billions of users and publishers across the globe every day. We embrace state-of-the-art machine learning technologies and scale them to detect and improve the tremendous amount of data generated on the platform. With the continuous efforts of our team, TikTok can provide the best user experience and bring joy to everyone in the world.

We are looking for people like you with solid experience in designing and deploying state-of-the-art models in the combination of NLP and CV-related areas. This position will work with a team of excellent research scientists and machine learning engineers who can take initiative, design and develop advanced machine learning solutions, and deploy them directly to TikTok's global platform.

Responsibilities - What You'II Do
1. Lead the design, training, and deployment of foundation models, including LLM/VLM, to support a broad range of content safety tasks across modalities. Build general-purpose foundation models with centralized compute and scalable architecture, aimed at improving risk detection, compliance understanding, and moderation automation.
2. Tackle challenges in multilingual, multimodal, and low-resource scenarios by enhancing models' zero-shot and few-shot generalization across diverse safety domains.
3. Design and maintain a multimodal safety annotation framework, supporting high-quality training data and evaluation signals for both image and video understanding tasks. Explore and implement RLHF strategies to fine-tune model alignment with evolving safety policies and user intent.
4. Collaborate closely with infra, data, and platform teams to optimize large-scale training pipelines, improve model serving efficiency, and leverage centralized GPU resources effectively.
5. Partner with product, policy, and recommendation teams to integrate safety-oriented models into real-world moderation workflows, and continuously optimize for performance and interpretability.

Qualifications

Qualifications

Minimum Qualifications:
1. Solid experience in traditional machine learning, with over 2 years of research or development experience in large models; passion for technology, and ability to dive deep into coding and debugging.
2. Strong problem-solving skills in the face of complex challenges, with a proven track record of technical breakthroughs and business impact; clear logic and sharp insight.
3. Highly responsible, self-driven, with excellent learning and communication abilities; strong interest in staying up-to-date with advances in multimodal large models.
4. A strong team player, open to exploring new technologies and driving continuous technical evolution.

Preferred Qualifications:
1. Publications in top conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV) or experience in competitive AI challenges.