About the job
Are you passionate about pushing the boundaries of recommendation systems? Do you dream of working on cutting-edge technologies that shape the way hundreds of millions of people discover content? If so, we invite you to join TikTok's US Core Recommendation Team as a PhD student and embark on an exciting journey of innovation. Our team's mission is to elevate TikTok’s personalized content discovery and user experiences to unprecedented heights. By constantly stretching the limits of deep learning and large-scale system design, we’re determined to make remarkable strides in recommendation precision, user involvement, and scalability, all to cater to the needs of hundreds of millions of users in the US.
Responsibilities
Conduct in-depth research and development in the aforementioned groundbreaking directions, designing and implementing innovative algorithms to enhance recommendation performance and accuracy.
Analyze large-scale user behavior data and content data to gain insights and drive model improvements.
Participate in the deployment and evaluation of the developed recommendation systems in real-world scenarios, ensuring their practical effectiveness.
Collaborate with cross-disciplinary teams, including infrastructure engineers, PMO, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.
Qualifications
Minimum
Currently pursuing a PhD degree in Computer Science, Electrical Engineering, Statistics, or a related field, with a focus on recommendation systems, natural language processing, or multimodal learning.
Strong theoretical foundation and hands-on research experience in relevant areas.
Proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.
Solid foundation in data structures, algorithms, and analytical and problem solving skills.
Preferred
First-author publications in top-tier conferences such as NeurIPS, ICML, ACL, CVPR, or KDD.
Experience with large-scale machine learning systems or applied research in industry.
Prior work or research integrating LLMs or multimodal models into real-world applications.
Familiarity with reinforcement learning, bandit algorithms, or offline RL for recommender systems.