Backend Software Engineer Intern (Foundational Technology) - 2026 Start (BS/MS)

Team Introduction Our team is responsible for building ByteDance's cloud-native infrastructure, which powers all of the company's products. We specialize in cutting-edge technologies in areas such as Kubernetes cluster management, runtime resource optimization, multi-cloud and multi-cluster solutions, and cloud-native infrastructure stability. The team has released open-source projects such as Kubebrain and Katalyst, and we are focused on groundbreaking research and development of core technologies around resource pooling and elasticity. We are looking for talented individuals to join us for an internship in 2026. Internships at ByteDance aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at ByteDance. 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 ByteDance and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Successful candidates must be able to commit to at least 3 months long internship period. Successful candidates must be able to commit to either of the following Internships - From January to June (onboard by 12 Jan 2026) - From May to August (onboard by 11 May 2026) Key Responsibilities - Build and Optimize Large-Scale Kubernetes Clusters; - Design and evolve system architectures for ultra-large-scale Kubernetes clusters, - Continuously improve the performance and stability of control systems in scenarios like big data and machine learning. - Define and Enhance Kubernetes SLOs; - Establish and optimize Service Level Objectives (SLOs) for Kubernetes clusters, - Analyze end-to-end latency, identify performance bottlenecks, propose solutions, and implement them in production environments. - Develop Observability for Kubernetes Clusters; - Build and enhance observability systems to improve issue diagnosis efficiency, - Create observability data warehouses and use data-driven approaches to optimize cluster performance.