Machine Learning Engineer Intern (Monetization Technology - Ads Core Global)- 2026 Start (PhD)

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 in 2026.

Internships at TikTok aim to offer students industry exposure and hands-on experience.

Watch your ambitions become reality as your inspiration brings infinite opportunities at TikTok.

PhD internships at TikTok provide 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).

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply.

The application limit is applicable to TikTok and its affiliates' jobs globally.

Successful candidates must be able to commit to at least 3 months long internship period.

Responsibilities:
As part of our team, you will be responsible for: 1.

Assist in optimizing efficiency across the entire advertising funnel, including Recall&Rough-sort, Fine-sort(CTR/CVR), format/creative personalization and system resource allocation.
2.

Research & develop a global advanced advertising delivery system through frontier technologies, including ML/DL, RL, LLM and also scaling law in ads recommendation.
3.

Design & Set up system framework and standard to continuously improve overall efficiency and meet different vertical business needs.

4.

Work with product and business teams from various scenarios with global impact.



Minimum Qualifications:
1.

Currently pursuing a PhD Degree in Computer Science, Mathematics, Statistics, or a related technical discipline with 2+ years research or machine learning modeling experience.
2.

Able to commit to working for 12 weeks during Summer 2026.
3.

Solid programming skills, proficient in C/C++ and Python.

Familiar with basic data structure and algorithms.

Familiar with Linux development environment.
4.

Good analytical thinking capability.

Essential knowledge and skills in statistics.
5.

Good theoretical grounding in deep learning concepts and techniques.
6.

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: 1.

PhD candidate focused on a statistical learning related field.

2.

Graduating December 2026 onwards with the intent to return to degree program after the completion of the internship.
3.

Good knowledge in one of the following fields: Factorization Machine, Uplift Modeling, Diffusion Models, Reinforcement Learning.
4.

Basic understanding of large recommendation system and ads serving system concepts.

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