Backend Engineer Graduate (TikTok Vertical Recommendation Architecture, Content Intelligence) - 2026 Start (BS/MS)

Responsibilities

About the Team
Our Content Understanding Architecture Team is responsible for building highly available and scalable content understanding system architectures in areas such as training, inference, and application. Collaborating with the Content Understanding Algorithm Team in areas such as Videos and LIVE, we are committed to efficiently applying large content understanding models(LLMs) in scenarios such as recommendation, security, and push notifications, providing TikTok users with the most stable and optimal experience. We are dedicated to enhancing the stability and reliability of content understanding architecture, continuously improving the performance of the content understanding architecture system through large model(LLMs) training and inference, and creating an industry-leading content understanding architecture.

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
- Participate in content understanding training and inference architecture design to improve the development efficiency, performance, scalability, and stability of the system.
- Build a highly available and low-latency content understanding inference architecture to support online and offline training and inference of TikTok's massive content.
- Analyze the performance of training and inference architectures, and optimize the efficiency of large model(LLMs) training and inference on GPU-based systems.
- Track cutting-edge technologies and applications within the industry, drive the upgrade of the team's technical architecture, and continuously expand application scenarios for content understanding.
- Collaborate with colleagues to serve global users and tackle the challenges brought by globalized architecture.

Qualifications

Minimum Qualifications:
- Final year or recent graduate with a background in Software Development, Computer Science, Computer Engineering, Electrical Engineering or other related technical discipline.
- Familiar with the design of high-concurrency content processing systems, and proficient in distributed frameworks (Flink/Spark) and message queues (Kafka/Pulsar).
- Strong software programming capabilities, exhibits good code design and coding style, familiarity with at least one of the programming languages: C++/Python/Java.
- Have a strong interest in large model training, inference, and optimization, with relevant experience preferred.

Preferred Qualifications:
- Interested in the direction of content understanding and have practical experience in content understanding reasoning or streaming data processing are preferred.
- Experience with large language models (LLMs), including Model Training, model inference, efficiency, and throughput optimization.