Applied Scientist Intern (Recommendation AI Lab) - 2026 Start (PhD) San Jose PhD Intern - 2026 Start

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

Benefits offered by this job

Health insurance from day one

Wellbeing benefits

Housing allowance eligibility for non-

Job summary

TikTok invites an Applied Scientist Intern to join the Recommendation AI Lab in San Jose for a 2026 start. You will work on foundation models for recommender and language tasks, aiming to advance monetization platforms via generative tech in search, recommendation, and advertising.

Responsibilities include scaling laws research, building a foundation model, and optimizing training/inference for real-time deployment, with a focus on long-term value and ROAS.

Qualifications

• Currently pursuing a PhD in Computer Science, Computer Engineering, or a related technical discipline.

Responsibilities

• Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
• Build an intelligent ad placement system optimized for users' Long-Term Value (LTV) and long-term ROAS, balancing commercial value and user experience.
• Optimize the full-process training and online inference framework for foundation models, balancing computing power costs and real-time response performance.

Skills

PhD candidate

C/C++

Python

NLP/CV

LLMs

Algorithm optimization

Education

PhD in Computer Science or related

Tools

PyTorch

TensorFlow

LLM frameworks

Job description

Applied Scientist Intern (Recommendation AI Lab) - 2026 Start (PhD)

Location: San Jose

Employment Type: Intern

Job Code: A125123

This team builds next-generation monetization platforms for TikTok ads, focusing on generative technologies in search, recommendation, and advertising. The internship focuses on foundation models for recommender and language tasks.

Responsibilities

• Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
• Build an intelligent ad placement system optimized for users' Long-Term Value (LTV) and long-term ROAS, achieving an optimal balance between commercial value and user experience.
• Optimize the full-process training and online inference framework for foundation models, balance computing power costs and real-time response performance, and resolve the performance‑latency trade‑off in real-world deployment.

Qualifications

• Currently pursuing a PhD in Computer Science, Computer Engineering, or a related technical discipline.
• Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV.
• Solid foundation in algorithms related to LLMs, including comprehensive learning and practical experience in areas such as single-modal LLM application and deployment.
• Priority for candidates with research results and extensive practical experience in natural language processing, computer vision, data modeling, or algorithm optimization.
• Excellent programming abilities with strong command of data structures and fundamental algorithms. Proficiency in C/C++ for traditional coding roles; proficiency in Python for intelligent coding roles.
• Strong publication record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, CVPR, ICCV, ECCV) is a plus.

Job Information

Hourly Rate: $60- $60.

Benefits

• Day one access to health insurance, life insurance, wellbeing benefits and more.
• 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half).
• Eligibility for housing allowance for interns not working 100% remote.

Equal Employment Opportunity Statement

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.