Research Scientist Graduate (TikTok Recommendation-Large Recommender Models) - 2026 Start (PhD)

Responsibilities

You'll be joining the TikTok Recommendation team focusing on advancing large-scale recommender systems that power TikTok's personalized content discovery and user experiences. By developing cutting-edge models, we aim to optimize recommendation accuracy, user engagement, and scalability across billions of users. We're looking for Machine Learning Scientists passionate about building high-performance, scalable recommendation systems. You'll leverage advanced deep learning techniques and large-scale systems engineering, collaborating with cross-functional teams to solve complex challenges in personalization and recommendation at scale.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.

Successful candidates must be able to commit to an onboarding date by end of year 2026.

We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Responsibilities
1. Research and develop large-scale recommender systems for personalized, engaging user experiences, focusing on scalability, accuracy, and performance.
2. Apply advanced machine learning and deep learning techniques to optimize recommendation algorithms for TikTok's diverse user base.
3. Manage the end-to-end lifecycle of recommender models, from training and fine-tuning to deployment, monitoring, and continuous improvement.
4. Analyze complex data to uncover user preferences, behaviors, and trends, driving personalization and enhancing TikTok's recommendation capabilities.
5. Collaborate with cross-functional teams (infrastructure, product, research, etc.) to design and implement innovative solutions that improve the relevance and diversity of TikTok recommendations.

Qualifications

Minimum Qualifications
1. Individuals who are completing or have recently completed a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
2. Experience in one or more areas of recommender systems, machine learning, computer vision, or natural language processing.
3. Proficiency in programming skills, solid foundation in data structures and algorithms.
4. Strong familiarity with deep learning architectures such as transformers, CNNs, RNNs, LSTMs, etc.
5. Excellent analytical and problem-solving skills, with the ability to collaborate effectively in cross-functional teams.

Preferred Qualifications
1. Ph.D. in Computer Science, Electrical Engineering, or related fields.
2. Experience in building large-scale recommender systems that handle vast, diverse datasets and complex user interactions.
3. Publications in major AI venues such as RecSys, SIGGRAPH, CVPR, ICCV, ICML, NeurIPS, ICLR, or similar conferences/journals.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $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

You'll be joining the TikTok Recommendation team focusing on advancing large-scale recommender systems that power TikTok's personalized content discovery and user experiences. By developing cutting-edge models, we aim to optimize recommendation accuracy, user engagement, and scalability across billions of users. We're looking for Machine Learning Scientists passionate about building high-performance, scalable recommendation systems. You'll leverage advanced deep learning techniques and large-scale systems engineering, collaborating with cross-functional teams to solve complex challenges in personalization and recommendation at scale.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.

Successful candidates must be able to commit to an onboarding date by end of year 2026.

We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Responsibilities
1. Research and develop large-scale recommender systems for personalized, engaging user experiences, focusing on scalability, accuracy, and performance.
2. Apply advanced machine learning and deep learning techniques to optimize recommendation algorithms for TikTok's diverse user base.
3. Manage the end-to-end lifecycle of recommender models, from training and fine-tuning to deployment, monitoring, and continuous improvement.
4. Analyze complex data to uncover user preferences, behaviors, and trends, driving personalization and enhancing TikTok's recommendation capabilities.
5. Collaborate with cross-functional teams (infrastructure, product, research, etc.) to design and implement innovative solutions that improve the relevance and diversity of TikTok recommendations.

Qualifications

Qualifications

Minimum Qualifications
1. Individuals who are completing or have recently completed a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
2. Experience in one or more areas of recommender systems, machine learning, computer vision, or natural language processing.
3. Proficiency in programming skills, solid foundation in data structures and algorithms.
4. Strong familiarity with deep learning architectures such as transformers, CNNs, RNNs, LSTMs, etc.
5. Excellent analytical and problem-solving skills, with the ability to collaborate effectively in cross-functional teams.

Preferred Qualifications
1. Ph.D. in Computer Science, Electrical Engineering, or related fields.
2. Experience in building large-scale recommender systems that handle vast, diverse datasets and complex user interactions.
3. Publications in major AI venues such as RecSys, SIGGRAPH, CVPR, ICCV, ICML, NeurIPS, ICLR, or similar conferences/journals.