PhD Intern – Machine Learning Recommendation

Job Description

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

The Tone:
This is a PhD internship at TikTok, with potential locations including Los Angeles. TikTok is a leading destination for short-form mobile video, with global headquarters in Los Angeles and Singapore. The Global E-Commerce Content Recommendation team plays a central role in the company, driving critical product decisions and platform growth. This internship offers students the opportunity to actively contribute to TikTok’s products and research, influencing the organization’s future plans and emerging technologies.

The TL;DR
• Role: Internship
• Location: Potential locations include Los Angeles
• Pay: $60 hourly
• Team: Global E-Commerce Content Recommendation team, comprised of machine learning researchers and engineers
• Mission: Innovate on production recommendation models and drive product impact by improving user experience, the content ecosystem, and platform security.
• Tech Stack: PyTorch, TensorFlow, C/C++, Python, shell programming languages

What You’ll Actually Do
• Build industry-leading recommendation systems to improve user experience, content ecosystem, and platform security.
• Explore generative recommendation techniques, including Diffusion Models, prompt learning, and multimodal content generation, to unlock new content discovery capabilities.
• Build multi-model and cross-scenario systems to enable unified recommendation across livestreams, short videos, and search.
• Deliver end-to-end machine learning solutions to address critical product challenges.
• 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.
• Skills: Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch, TensorFlow) and familiarity with deep neural network architectures.
• Bonus: Research experience in applied machine learning, machine learning infrastructure, large-scale recommendation systems, or market-facing machine learning products; strong first-author publications record in AI conferences or journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.); proficient in C/C++, Python, and shell programming languages with a deep understanding of data structure and algorithm design; internship experience in an AI research organization.