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Research Scientist Graduate (TikTok Recommendation-LLMs, RL, GenAI) - 2026 Start (PhD)

AT TikTok
TikTok

Research Scientist Graduate (TikTok Recommendation-LLMs, RL, GenAI) - 2026 Start (PhD)

San Jose, CA

Responsibilities

Are you passionate about pushing the boundaries of recommendation systems? Do you dream of working on cutting-edge technologies that shape the way hundreds of millions of people discover content? If so, we invite you to join TikTok's US Core Recommendation Team as a PhD student and embark on an exciting journey of innovation.

Our team's mission is to elevate TikTok's personalized content discovery and user experiences to unprecedented heights. By constantly stretching the limits of deep learning and large-scale system design, we're determined to make remarkable strides in recommendation precision, user involvement, and scalability, all to cater to the needs of hundreds of millions of users in the US.

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As a PhD student in our team, you will be at the forefront of developing the next generation of recommendation systems. Your work will be pivotal in enhancing the user experience by delivering more accurate, personalized, and engaging content recommendations. You will have the opportunity to delve into several groundbreaking directions, including but not limited to:
- End-to-End Generative Large Recommendation Systems: We are committed to reimagining the traditional recommendation pipelines. You will explore novel architectures, algorithms, and optimization strategies to break through the limitations of existing systems. By challenging the status quo, you will strive to build more efficient, scalable, and generative recommendation frameworks.
- Ultra-Long Sequence Modeling of User Lifecycle Behavior: Understanding user behavior over an extended period is crucial for providing long-term personalized recommendations. You will focus on modeling the ultra-long sequences of user interactions throughout their lifecycle on TikTok.
- Integrating LLM and Multimodal Technologies for Recommendation: With the abundance of multimodal content (text, image, video, audio) on TikTok, integrating LLM and multimodal technologies into recommendation systems is essential. You will work on leveraging the power of LLMs to understand and process information, and combine it with other multimodal data to enable seamless multimodal-recommendation fusion.
- Posttraining & RL: Exploration of posttraining methods to better align large generative models with business and feed quality needs. Conduct original research on applying RL (e.g., bandit models, policy optimization, offline RL) to recommendation problems (such as diversity & multiobjective fusion problems)

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
- Conduct in-depth research and development in the aforementioned groundbreaking directions, designing and implementing innovative algorithms to enhance recommendation performance and accuracy.
- Analyze large-scale user behavior data and content data to gain insights and drive model improvements.
- Participate in the deployment and evaluation of the developed recommendation systems in real-world scenarios, ensuring their practical effectiveness.
- Collaborate with cross-disciplinary teams, including infrastructure engineers, PMO, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.

Qualifications

Minimum Qualifications
- Currently pursuing a PhD degree in Computer Science, Electrical Engineering, Statistics, or a related field, with a focus on recommendation systems, natural language processing, or multimodal learning.
- Strong theoretical foundation and hands-on research experience in relevant areas.
- Proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
- Solid foundation in data structures, algorithms, and analytical and problem solving skills.

Preferred Qualifications
- First-author publications in top-tier conferences such as NeurIPS, ICML, ACL, CVPR, or KDD.
- Experience with large-scale machine learning systems or applied research in industry.
- Prior work or research integrating LLMs or multimodal models into real-world applications.
- Familiarity with reinforcement learning, bandit algorithms, or offline RL for recommender systems.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $136800 - $359720 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.

Client-provided location(s): San Jose, CA, USA
Job ID: TikTok-7527444694349367559
Employment Type: Other

Perks and Benefits

  • Health and Wellness

    • Health Insurance
    • Dental Insurance
    • Vision Insurance
    • HSA
    • Life Insurance
    • Fitness Subsidies
    • Short-Term Disability
    • Long-Term Disability
    • On-Site Gym
    • Mental Health Benefits
    • Virtual Fitness Classes
  • Parental Benefits

    • Fertility Benefits
    • Adoption Assistance Program
    • Family Support Resources
  • Work Flexibility

    • Flexible Work Hours
    • Hybrid Work Opportunities
  • Office Life and Perks

    • Casual Dress
    • Snacks
    • Pet-friendly Office
    • Happy Hours
    • Some Meals Provided
    • Company Outings
    • On-Site Cafeteria
    • Holiday Events
  • Vacation and Time Off

    • Paid Vacation
    • Paid Holidays
    • Personal/Sick Days
    • Leave of Absence
  • Financial and Retirement

    • 401(K) With Company Matching
    • Performance Bonus
    • Company Equity
  • Professional Development

    • Promote From Within
    • Access to Online Courses
    • Leadership Training Program
    • Associate or Rotational Training Program
    • Mentor Program
  • Diversity and Inclusion

    • Diversity, Equity, and Inclusion Program
    • Employee Resource Groups (ERG)

Company Videos

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