Machine Learning Intern – Advertising Machine Learning

Job Description

Machine Learning Engineer Intern (Monetization Technology – Ads Core Global) | TikTok

The Tone:
This is a temporary internship at TikTok, available in various global offices with in-person or hybrid work expected. TikTok is a leading global destination for short-form mobile video, developing advertising as a global business. This role is crucial for optimizing the entire advertising funnel by establishing a world-leading ranking model and framework, ensuring better returns for collaborators, users, and customers worldwide.

The TL;DR
• Role: Internship
• Type: Temporary (Summer)
• Location: Various global offices (in-person/hybrid expected)
• Pay: $45–$60 hourly
• Team: Ads Core ML Team within Monetization Technology
• Mission: To optimize efficiency across the entire advertising funnel and develop a global advanced advertising delivery system using frontier technologies to benefit collaborators, users, and customers.
• Tech Stack: C/C++, Python, TensorFlow/Pytorch/MXNet, Linux

What You’ll Actually Do
• Optimize: Assist in improving 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 global advanced advertising delivery systems using frontier technologies such as machine learning, deep learning, reinforcement learning, large language models, and scaling laws in ad recommendations.
• Design: Design and establish system frameworks and standards to continuously enhance overall efficiency and cater to diverse vertical business needs.
• Collaborate: Work with product and business teams on various scenarios that have a global impact.

The Must-Haves
• Background: Student currently pursuing an Undergraduate or Master’s Degree in Computer Science, Mathematics, Statistics, or a related technical discipline.
• Experience: Solid programming proficiency in C/C++ and Python, with familiarity in basic data structures, algorithms, and the Linux development environment. Possesses a strong theoretical understanding of deep learning concepts and techniques, along with familiarity in the architecture and implementation mechanisms of at least one mainstream machine learning framework (TensorFlow/Pytorch/MXNet).
• Skills: Analytical thinking capability, essential knowledge and skills in statistics.
• Bonus: Good knowledge in fields such as Factorization Machines, Uplift Modeling, Diffusion Models, or Reinforcement Learning. Basic understanding of large recommendation systems and ad serving system concepts.