PhD Intern, Recommendation Algorithm Team

Job Description

Machine Learning Scientist Intern (TikTok-Recommendation) – 2026 Start (PhD) | TikTok

The Tone:
This is an in-person internship at TikTok, available in one of its global offices. The company is a leading destination for short-form mobile video, inspiring creativity and bringing joy through its innovative product. The recommendation algorithm team plays a central role, driving critical product decisions and platform growth. This role contributes to the organization’s future plans and emerging technologies through active participation in product development and research.

The TL;DR
• Role: Internship
• Type: Temporary
• Location: Location not specified in the primary job details
• Pay: $60 hourly
• Team: Recommendation algorithm team, made up of machine learning researchers and engineers
• Mission: Drive the development of industry-leading recommendation systems that elevate user experience, strengthen platform safety, and empower a vibrant content ecosystem.
• Tech Stack: PyTorch, TensorFlow, C/C++, Python, shell programming languages

What You’ll Actually Do
• Drive Development: Drive the development of industry-leading recommendation systems that elevate user experience, strengthen platform safety, and empower a vibrant content ecosystem.
• Deliver Solutions: Deliver impactful, end-to-end machine learning solutions that tackle high-priority product challenges related to content understanding, LLMs, robustness, and fairness.
• Own and Optimize: Own and optimize the full-stack ML pipeline—from algorithm design to system infrastructure—to continuously push the boundaries of recommendation performance.
• Collaborate with Teams: Collaborate with cross-functional teams to craft innovative product strategies and develop intelligent solutions that fuel TikTok’s growth in key global markets.

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, potentially including research experience in applied machine learning, machine learning infrastructure, large-scale recommendation systems, or market-facing machine learning products.
• Skills: Solid knowledge and experience with at least one popular deep learning framework (e.g., PyTorch, TensorFlow), familiarity with deep neural network architectures, and proficiency in C/C++, Python, and shell programming languages with a deep understanding of data structure and algorithm design.
• Bonus: Strong first-author publications record in accredited AI conferences or journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL etc.), or internship experience in an AI research organization.