Research Scientist Intern (Monetization Technology - Ads Core) - 2027 Start (PhD)

Responsibilities

TikTok Ads Core ML Team aims at creating automatic delivery products for the next generation and developing advertising as a global business, instead of just a monetization tool to consolidate the delivery funnel framework allowing multiple teams to iterate parallel. All of our team effort, is to continuously pursue and establish a world-leading ranking model & framework that always benefits our collaborators, users and customers to get better returns.

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

Responsibilities:
- Assist in optimizing efficiency across the entire advertising funnel, including Recall&Rough-sort, Fine-sort(CTR/CVR), format/creative personalization and system resource allocation.
- Research & develop a global advanced advertising delivery system through frontier technologies, including ML/DL, RL, LLM and also scaling law in ads recommendation.
- Design & Set up system framework and standard to continuously improve overall efficiency and meet different vertical business needs.
- Work with product and business teams from various scenarios with global impact.

Qualifications

Minimum Qualifications:
- Currently pursuing a PhD in Computer Science, Mathematics, Statistics, or a related technical discipline.
- Solid programming skills, proficient in C/C++ and Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
- Good analytical thinking capability. Essential knowledge and skills in statistics.
- Good theoretical grounding in deep learning concepts and techniques.
- Familiar with architecture and implementation of at least one mainstream machine learning programming framework (TensorFlow/Pytorch/MXNet), familiar with its architecture and implementation mechanism.

Preferred Qualifications:
- Good knowledge in one of the following fields: Factorization Machine, Uplift Modeling, Diffusion Models, Reinforcement Learning.
- Basic understanding of large recommendation system and ads serving system concepts.