Big Data Engineer Graduate (TikTok Live Recommendation Architecture) - 2026 Start (BS/MS)

About the Team
Our TikTok Live Recommendation Architecture Team is responsible for building up and optimizing the architecture for live broadcast recommendation system to provide the most stable and best experience for our TikTok users. The team is responsible for system stability and high availability, online services and offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid-platform, realizing flexible and scalable high-performance storage and computing systems. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques.

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
- Design and implement reasonable offline data architecture for large-scale recommendation systems.
- Design and implement flexible, scalable, and high-performance storage and computing systems.
- Trouble-shooting of the production system, design and implement the necessary mechanisms and tools to ensure the stability of the overall operation of the production system.
- Build industry-leading distributed systems such as storage and computing to provide reliable infrastructure for massive data and large-scale business systems.
- Develop and implement techniques and analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualisation software.
- Apply data mining, data modeling, natural language processing, and machine learning to extract and analyse information from large structured and unstructured datasets.
- Visualise, interpret, and report data findings and may create data reports as well.