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

TikTok; San Jose (CA); On-site; USD 68,191 - 97,120; Full time

Benefits offered by this job

Health insurance

10 paid holidays

Paid sick time

Housing allowance (if applicable)

Job summary

TikTok is seeking a PhD Intern to join its US Core Recommendation Team in San Jose, California. This role involves conducting cutting-edge research in recommendation systems, focusing on areas such as large-scale data analysis and multimodal integration.

The ideal candidate is currently pursuing a PhD in Computer Science or related fields and has strong programming skills in Python. The position offers an hourly rate of $60 and provides access to health benefits and paid holidays.

Qualifications

• Currently pursuing a PhD degree in a relevant field.
• Hands-on research experience in recommendation systems.
• Strong foundation in data structures and algorithms.

Responsibilities

• Conduct research in recommendation systems.
• Analyze large-scale user behavior data.
• Collaborate with cross-disciplinary teams.

Skills

Research in recommendation systems

Proficiency in Python

Knowledge of ML frameworks

Analytical problem-solving skills

Education

Currently pursuing a PhD degree

Tools

PyTorch

TensorFlow

Job description

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.

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 – exploring novel architectures, algorithms, and optimization strategies to build efficient, scalable, and generative recommendation frameworks.
• Ultra‑Long Sequence Modeling of User Lifecycle Behavior – modeling the ultra‑long sequences of user interactions throughout their lifecycle on TikTok.
• Integrating LLM and Multimodal Technologies for Recommendation – leveraging LLMs and multimodal data to enable seamless multimodal‑recommendation fusion.
• Post‑training & RL – exploring post‑training methods to align large generative models with business and feed quality needs, and conducting original research on applying RL (bandit models, policy optimization, offline RL) to recommendation problems such as diversity and multi‑objective fusion.

We are looking for talented individuals to join our team in 2026. As a PhD Intern, you will get unparalleled opportunities to kickstart your career, pursue bold ideas, and explore limitless growth opportunities.

Key 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.

Compensation and Benefits

【For Pay Transparency】Compensation Description (Hourly) – Campus Intern

The hourly rate range for this position in the selected city is $60 – $60.

Benefits may vary depending on the nature of employment and the country work location. Interns have day one access to health insurance, life insurance, wellbeing benefits and more. Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half). Interns who are not working 100% remote may also be eligible for housing allowance.

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

Equal Employment Opportunity & Fair Chance

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:

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

Accommodations

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request