Applied Scientist Intern - Monetization Technology - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

Responsibilities

We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.
PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Team Introduction: Global Monetization Product and Technology team are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

Topic Content: This topic dives deep into TikTok's core global advertising scenarios, driving innovation and implementation of the cutting-edge generative technologies in search, recommendation, and advertising. By deeply integrating foundation models with the advertising business, we address key technical challenges in Large Recommender Models and Large Language Models (LLMs) to build a next-generation intelligent advertising engine with autonomous decision-making capabilities.

Our research covers cutting-edge directions, including Large Recommender Model scaling laws, end-to-end unified modeling, generative full-link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra-long sequence modeling, and causal inference. We tackle extreme challenges of trillion-level features and millisecond responses, advancing advertising recommendation toward the foundation model paradigm to achieve dual improvements in monetization efficiency and user experience.

Responsibilities:
1. Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
2. 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.