TikTok E-commerce is committed to building a global commerce ecosystem where content and commerce are deeply integrated, delivering a next-generation shopping experience to users. Our Recommendation team plays a critical role in enabling personalized and engaging user journeys by leveraging cutting-edge machine learning technologies. In today’s content-driven commerce landscape, traditional collaborative filtering and supervised learning methods are no longer sufficient. We’re actively exploring how large language models (LLMs) and generative AI can fundamentally transform the recommendation process: from retrieval to ranking, and from static listings to dynamic, generative user-item interactions. 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 TikTok. 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 - Develop and deploy ML models to power personalized e-commerce recommendations - Collaborate cross-functionally with product, infra, and data teams to translate business goals into technical solutions - Evaluate model performance in both offline and online (A/B) testing to drive user experience and GMV - Focused on scaling, robustness, and production-quality deployment
Minimum Qualifications: - PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related technical field - Proficient coding skills in Python and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch - Demonstrated ability to conduct rigorous research and analyze large-scale data - Strong problem-solving skills and a high sense of ownership Preferred Qualifications: - Publications in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ACL, SIGIR, KDD, CVPR, RecSys) - Experience with recommendation systems, retrieval models, or multi-modal learning - Familiarity with building and deploying real-time, scalable ML systems in production - Background in e-commerce or related applied AI research domains
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