PhD Intern – Generative AI, Ads & Recommendation

Job Description

Applied Scientist Intern – Monetization Technology – Global Frontier Tech Recruitment Program – 2027 Start (PhD) | TikTok

The Tone:
This is a PhD internship at TikTok, likely based in Los Angeles, where students contribute to the company’s products, research, and emerging technologies. TikTok is a leading global destination for short-form mobile video, inspiring creativity and bringing joy to users worldwide. This role is crucial for the Global Monetization Product and Technology team, which is building next-generation monetization platforms to help businesses grow using TikTok’s products. The intern will drive innovation by deeply integrating cutting-edge generative technologies and foundation models into TikTok’s core global advertising scenarios.

The TL;DR
• Role: Internship (PhD)
• Location: In-person, Los Angeles, CA
• Pay: $60 hourly
• Team: Global Monetization Product and Technology team
• Mission: Drive innovation in advertising by integrating generative technologies and foundation models into TikTok’s core monetization platforms.
• Tech Stack: Large Recommender Models, Large Language Models (LLMs), C/C++, Python

What You’ll Actually Do
• Model Exploration: Explore scaling laws for foundation models within recommendation and advertising, constructing a unified multimodal semantic modeling foundation model.
• System Development: Build an intelligent ad placement system focused on optimizing users’ Long-Term Value (LTV) and long-term Return On Ad Spend (ROAS).
• Performance Optimization: Optimize the full-process training and online inference framework for foundation models, balancing computing power costs with real-time response performance.

The Must-Haves
• Background: Currently pursuing a PhD in Computer Science, Computer Engineering, or a closely related technical discipline.
• Experience: Modeling experience in advertising, search engines, recommender systems, Natural Language Processing (NLP), or Computer Vision (CV). Practical experience in single-modal LLM application and deployment is required.
• Skills: A solid foundation in algorithms related to Large Language Models (LLMs), including comprehensive learning. Strong command of data structures and fundamental algorithms, with proficiency in C/C++ for traditional coding roles or Python for intelligent coding roles.
• Bonus: Outstanding research results and extensive practical experience in fields such as natural language processing, computer vision, data modeling, or algorithm optimization. A strong publication record in top conferences such as ICLR, NeurIPS, ICML, ACL, EMNLP, NACCL, CVPR, ICCV, and ECCV.