PhD Intern – Machine Learning Recommendations

Job Description

Machine Learning Scientist Intern (Global E-Commerce Content Recommendation) – 2026 Start (PhD) | TikTok

The Tone:
This is a PhD internship at TikTok, primarily located in Los Angeles County (unincorporated), with opportunities to contribute to a global e-commerce platform. TikTok is the leading destination for short-form mobile video, focused on inspiring creativity and bringing joy to its users worldwide. The Global E-Commerce Content Recommendation team plays a central role within the company by driving critical product decisions and platform growth. This role is essential for supporting and innovating on production recommendation models, directly impacting product success and the organization’s future plans.

The TL;DR
• Role: Internship
• Location: In-person, Los Angeles County (unincorporated)
• Pay: $60 hourly
• Team: Global E-Commerce Content Recommendation team, comprised of machine learning researchers and engineers.
• Mission: This person solves critical product challenges by building and optimizing industry-leading machine learning recommendation systems to enhance user experience and platform growth.
• Tech Stack: PyTorch, TensorFlow, C/C++, Python, shell programming languages

What You’ll Actually Do
• Build: Build industry-leading recommendation systems to improve user experience, content ecosystem, and platform security.
• Explore: Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new capabilities in content discovery.
• Design: Build multi-model and cross-scenario systems enabling unified recommendation across livestreams, short videos, and search.
• Deliver: Deliver end-to-end machine learning solutions to address critical product challenges.
• Optimize: Own the full stack machine learning system and optimize algorithms and infrastructure to improve recommendation performance.

The Must-Haves
• Background: Currently pursuing a PhD with a background in computer science, machine learning, or similar fields.
• Experience: Good knowledge of theoretical and empirical research in addressing research problems, along with familiarity with deep neural network architectures.
• Skills: Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch, TensorFlow).
• Bonus: Research experience in applied machine learning, machine learning infrastructure, large-scale recommendation systems, or market-facing machine learning products; a strong first-author publications record in AI conferences or journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.); proficiency in C/C++, Python, and shell programming languages with a deep understanding of data structure and algorithm design; or prior internship experience in an AI research organization.