Machine Learning Engineer Graduate (TikTok Shop Global E-Commerce, Recommendation Alliance) - 2026 Start (BS/MS)

Team Introduction
The alliance team aims to serve merchants and creators on the e-commerce platform to meet merchants' business indicators and improve creators' creative efficiency. By cooperating with merchants and creators, we aim to provide high-quality content and a personalized shopping experience for TikTok users, create efficient shopping tools at seller centers, and promote cooperation between merchants and creators.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok.

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.

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 TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities
- Build and design large-scale machine learning algorithms to model the real-time interests of creators to improve conversion efficiency.
- Optimize TikTok's massive commodity relationship mining and ranking and recall of creators' product recommendations, and improve the overall GMV of the platform.
- Use the large scale of data to deeply analyze the behavior of creators and merchants and build targeted algorithm strategies to empower creators and merchants, improve merchants' monetization efficiency, and boost industrial upgrading.
- Support the rapid development of TikTok's e-commerce business, serve millions of merchants, explore more efficient business models, satisfy users with the best shopping experience, and promote ecological development.
- Support the production of scalable and optimised AI/machine learning (ML) models.
- Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models.