(General Hire) Machine Learning Engineer (all levels) , TikTok Recommendation

About the Team Recommendation algorithm team plays a central role in the company, driving critical product decisions and platform growth. The team is a diverse group of world-level researchers and engineers, who develop and support the production recommendation systems that drive product impact. The team is fast-paced, collaborative and impact-driven.

What is General Hire ? If you re applying under the General Hire track for the TikTok Recommendation Organization, you ll go through a unified interview process designed to assess your fit across the whole TikTok Recommendation Organization. Based on your background, experience, interests, and interview performance, we ll help identify one or more teams that are the best match for you.

Responsibilities - What You II Do

• Develop recommendation systems that improve user experience and platform content ecosystem.
• Build machine learning systems and pipelines to address critical product challenges.
• Own a full stack machine learning system(s), lead its development, support, and improvements.
• Collaborate with cross functional teams to design product strategies and build solutions to grow TikTok in the US market. Minimum Qualifications
• B.S. in Computer Science, Machine Learning, Statistics, or equivalent experience.
• Strong coding skills in Python and/or C++, with solid foundations in data structures and algorithms.
• Experience building machine learning systems using frameworks such as PyTorch or TensorFlow.
• Proven expertise in recommendation systems, ranking, retrieval, personalization, applied machine learning, or large-scale ML infrastructure.
• Strong communication and collaboration skills.

Preferred Qualifications

• Ph.D. in Recommendation Systems, Machine Learning, or equivalent experience.
• Strong background in user modeling, recommendation, ranking, retrieval, or large-scale ML systems.
• Experience with LLM-powered recommendation, multimodal recommendation, or reinforcement learning.
• Publications in ML, AI, or RecSys conferences are a strong plus.