Research Scientist Graduate

About the Role

You'll be joining the TikTok Recommendation team, focusing on advancing large-scale recommender systems that power TikTok’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 are 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.

Key Responsibilities

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

Requirements & Tech Stack

• Individuals who are completing or have recently completed a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
• Experience in one or more areas of recommender systems, machine learning, computer vision, or natural language processing.
• Proficiency in programming skills, solid foundation in data structures and algorithms.
• Strong familiarity with deep learning architectures such as transformers, CNNs, RNNs, LSTMs, etc.
• Excellent analytical and problem-solving skills, with the ability to collaborate effectively in cross-functional teams.

Qualifications

• Ph.D. in Computer Science, Electrical Engineering, or related fields.
• Experience in building large-scale recommender systems that handle vast, diverse datasets and complex user interactions.
• Publications in major AI venues such as RecSys, SIGGRAPH, CVPR, ICCV, ICML, NeurIPS, ICLR, or similar conferences/journals.

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.