Machine Learning Intern – Ads Ranking ML

Job Description

Machine Learning Engineer Intern (Monetization Technology – Ads Core Global) – 2026 Summer (BS/MS) | TikTok

The Tone:
This is a temporary internship at TikTok, likely located in Los Angeles, CA. TikTok is the leading destination for short-form mobile video, with a mission to inspire creativity and bring joy globally. This role contributes to the Ads Core ML Team’s effort to develop automatic advertising delivery products and establish a world-leading ranking model and framework. Internships provide students with valuable industry exposure, hands-on experience, and opportunities to develop fundamental skills for future career paths.

The TL;DR
• Role: Internship
• Type: Temporary
• Location: In-person, Los Angeles, CA
• Pay: $45–$60 hourly
• Team: Ads Core ML Team (Monetization Technology – Ads Core Global)
• Mission: Continuously pursue and establish a world-leading ranking model and framework that benefits collaborators, users, and customers by improving advertising returns and delivery efficiency.
• Tech Stack: C/C++, Python, Linux, TensorFlow/PyTorch/MXNet

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

The Must-Haves
• Background: Currently pursuing an Undergraduate/Master Degree in Computer Science, Mathematics, Statistics, or a related technical discipline.
• Experience: Familiar with the Linux development environment.
• Skills: Solid programming skills proficient in C/C++ and Python, familiarity with basic data structures and algorithms, good analytical thinking capability, essential knowledge and skills in statistics, good theoretical grounding in deep learning concepts and techniques, and familiarity with the architecture and implementation mechanism of at least one mainstream machine learning programming framework (TensorFlow/PyTorch/MXNet).
• Bonus: Good knowledge in Factorization Machine, Uplift Modeling, Diffusion Models, or Reinforcement Learning, and a basic understanding of large recommendation systems and ads serving system concepts.