Machine Learning Engineer Graduate (Global E-Commerce) - 2027 Start

About the Team The E-commerce Algorithm Team powers TikTok Shop's intelligent commerce ecosystem, connecting users with quality products and sellers through personalized recommendations, search, live streaming, and short-form content. Our team of Machine Learning Engineers, Applied Scientists, and Data Scientists develops large-scale AI and machine learning solutions that enhance user experience, drive merchant growth, and create business impact. We work on cutting-edge technologies across recommendation systems, search, ranking, LLM applications, and marketplace optimization, transforming innovative research into products used by millions worldwide.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities 1. Design and develop ML algorithms and intelligent systems for large-scale e-commerce scenarios: recommendation, ranking, retrieval, forecasting, content understanding, pricing, governance, and conversational AI. 2. Apply machine learning, deep learning, NLP, CV, multimodal models, and LLMs to solve real-world business problems. 3. Build end-to-end algorithm solutions: data processing, feature engineering, model training, evaluation, deployment, monitoring, and continuous optimization. 4. Develop scalable systems for real-time or offline learning and inference on large-scale structured and unstructured data. 5. Partner with product, engineering, data science, and business teams to translate needs into high-impact technical solutions. 6. Run experiments, analyze model performance, debug production issues, and iterate rapidly to improve quality and efficiency. 7. Explore next-gen intelligent systems: foundation models, generative recommendation, multimodal reasoning, and AI agents.