Research Scientist Graduate ( Recommendation- Recommender Models PhD

Position: Research Scientist Graduate (TikTok Recommendation-Large Recommender Models) - 2026 Start (PhD)
You'll be joining the Tik Tok Recommendation team focusing on advancing large-scale recommender systems that power Tik Tok's personalized content discovery and user experiences. By developing cutting-edge models, we aim to optimize recommendation accuracy, user engagement, and scalability across billions of users. We're looking for Machine Learning Scientists passionate about building high-performance, scalable recommendation systems. You'll leverage advanced deep learning techniques and large-scale systems engineering, collaborating with cross-functional teams to solve complex challenges in personalization and recommendation at scale.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with Tik Tok.

Successful candidates must be able to commit to an onboarding date by end of year 2026.

We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Responsibilities
1. Research and develop large-scale recommender systems for personalized, engaging user experiences, focusing on scalability, accuracy, and performance.
2. Apply advanced machine learning and deep learning techniques to optimize recommendation algorithms for Tik Tok's diverse user base.
3. Manage the end-to-end lifecycle of recommender models, from training and fine-tuning to deployment, monitoring, and continuous improvement.
4. Analyze complex data to uncover user preferences, behaviors, and trends, driving personalization and enhancing Tik Tok's recommendation capabilities.
5. Collaborate with cross-functional teams (infrastructure, product, research, etc.) to design and implement innovative solutions that improve the relevance and diversity of Tik Tok recommendations.