Software Engineer - Model Infrastructure, TikTok Feeds

About the Team

The TikTok Feeds Recommendation Architecture team is responsible for the design and development of TikTok's For You Feed recommendation system. Our scope spans the entire online and offline recommendation pipeline, including strategy, modeling, and data infrastructure. We build scalable and efficient infrastructure to support rapid algorithm iteration, tackle massive throughput challenges, and enhance system performance, cost-efficiency, and stability. Our mission is to abstract and generalize core infrastructure services, components, and productivity tools that empower the recommendation system at scale.

About the Role

We are seeking talented Software Engineers to join our Model Infrastructure team. You will focus on the architecture and engineering challenges associated with large-scale recommendation models – from online inference to offline training. Your work will directly contribute to supporting increasingly complex models, solving bottlenecks in computation and storage, and enabling breakthrough innovations in recommendation algorithms.

As large foundation models (in CV/NLP/multimodal and beyond) continue to advance, the recommendation domain is evolving towards leveraging powerful compute to gain deeper understanding of user intent and preferences. In this role, you'll help co-design system architecture and algorithm infrastructure to balance compute cost with recommendation effectiveness at scale.

Responsibilities

• Build and optimize infrastructure for online inference and offline training of recommendation models. - Solve challenges posed by the combination of high model complexity, massive data, and large-scale deployments. - Work closely with algorithm researchers to co-design infrastructure aligned with cutting-edge machine learning frameworks and hardware accelerators. - Continuously improve model architecture, efficiency, and scalability to enable better user experiences. Abstract and build reusable infrastructure components and tools that benefit the broader recommendation stack.