PhD Intern – Machine Learning, Recommendation Systems

Job Description

Machine Learning Engineer Intern (E-Commerce Recommendation/Search Alliance) – 2026 Start (PhD) | ByteDance

The Tone:
This is a PhD internship at ByteDance, a company founded in 2012 with a mission to inspire creativity and enrich life. ByteDance builds a suite of over a dozen products, including TikTok, Lemon8, CapCut, Pico, Toutiao, Douyin, and Xigua. This role contributes to the E-commerce Alliance team, which serves merchants and creators on the platform. The work aims to enhance recommendation systems and analyze e-commerce data to provide high-quality content and a personalized shopping experience for TikTok users, while also contributing to the organization’s future plans and emerging technologies.

The TL;DR
• Role: Internship
• Location: Los Angeles, CA (potential other locations)
• Pay: $57 hourly
• Team: E-commerce Alliance team
• Mission: Design and implement machine learning algorithms to enhance e-commerce recommendation systems and analyze data for strategic insights.

What You’ll Actually Do
• Design: Implement cutting-edge machine learning algorithms to enhance existing recommendation systems.
• Apply: Utilize machine learning, natural language processing, and computer vision techniques to analyze e-commerce data and user interactions.
• Deploy: Maintain scalable recommendation models engineered for real-time query handling and effective product suggestions.
• Analyze: Examine complex datasets thoroughly to derive actionable insights that guide strategic business decisions.
• Stay Updated: Keep abreast of the latest machine learning developments and effectively incorporate them into project work.

The Must-Haves
• Background: Currently pursuing a PhD in Computer Science, Engineering, Operations Research, or a closely related field.
• Experience: Strong grasp of data structures, algorithms, and top-notch problem-solving skills.
• Skills: Profound knowledge in machine learning, statistics, and big data engineering.
• Bonus: Final year or recent graduate with a background in Software Development, Computer Science, Computer Engineering, or a related technical discipline, coupled with a deep understanding of scaling distributed computing.