The role
From TikTok's own posting.
About the Team
Our Content AI Infra 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 AI 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.
As a project intern, you will have the opportunity to engage in impactful short-term projects that provide you with a glimpse of professional real-world experience. You will gain practical skills through on-the-job learning in a fast-paced work environment and develop a deeper understanding of your career interests.
Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities
- Participate in content AI 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.
Minimum Qualification(s)
- Currently pursuing an Undergraduate/Master in Software Development, Computer Science, Computer Engineering, or a 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 Qualification(s)
- Interested in the direction of content understanding and has experience in recommender systems and large models(LLMs).
- Relevant experience in competitions (such as mathematical modeling, programming competitions) is a plus.
- Good collaborator and team player, comfortable working in a fast moving, culturally diverse and globally distributed team environment.