Software Engineer - AI Infrastructure Compute Efficiency

About the Team The Compute Infrastructure - Orchestration & Scheduling team uses Kubernetes and Serverless technologies to build a large, reliable, and efficient compute infrastructure. This infrastructure powers hundreds of large-scale clusters globally, with over millions of online containers and offline jobs daily, including AI and LLM workloads. The team is dedicated to building cutting-edge, industry-leading infrastructure that empowers AI innovation, ensuring high performance, scalability, and reliability to support the most demanding AI/LLM workloads.The team is also dedicated to open-sourcing key infrastructure technologies, including projects in the K8s portfolio such as kubewharf, Serverless initiatives like Ray on K8s, and LLM inference control plan project AiBrix. At ByteDance, as we expand and innovate, powering global platforms like TikTok and various AI/ML & LLM initiatives, we face the challenge of enhancing resource cost efficiency on a massive scale within our rapidly growing compute infrastructure. We're seeking talented software engineers excited to optimize our infrastructure for AI & LLM models. Your expertise can drive solutions to better utilize computing resources (including CPU, GPU, power, etc.), directly impacting the performance of all our AI services and helping us build the future of computing infrastructure. Also, with the goal of growing compute infrastructure in overseas regions, including North America, Europe, and Asia Pacific, you will have the opportunities of working closely with leaders from ByteDance’s global business units to ensure that we continue to scale and optimize our infrastructure globally. Responsibilities - Design and evolve the architecture of large-scale Kubernetes-based infrastructure platforms to ensure performance, scalability, and resilience for diverse workloads, including microservices, big data, and AI/LLM applications. - Improve K8s system performance across the control and data planes, including optimizing pod lifecycle, resource orchestration, and system-level throughput under high load. - Build robust observability and performance analysis frameworks, define K8s system-level SLOs, and lead data-driven tuning and optimization initiatives in production. - Develop intelligent, unified resource management and scheduling systems (at node & cluster-level) to support a wide range of compute resources in large-scale, cloud-native environments. - Drive the standardization and optimization of container runtime environments to enhance workload isolation, reliability, and resource efficiency across heterogeneous compute environments. Minimum Qualifications - B.S./M.S, degree in Computer Science, Computer Engineering or a related area with 3+ years of relevant industry experience; Ph.D. degree and strong publication records can be an exception. - Solid understanding of at least one of the following fields: Unix/Linux environments, distributed and parallel systems, high-performance networking systems, developing large scale software systems - Familiarity with container and orchestration technologies such as Docker and Kubernetes. - Proficiency in at least one major programming language such as Python, Go, C++, Rust, and Java. Preferred Qualifications - Knowledge of big data or machine learning workflows in a Kubernetes environment. - Experience in developing or contributing to cloud-native open-source projects. - Hands-on project experience with containerized applications through internships, coursework, or personal projects. - Familiarity with observability tools and frameworks like Prometheus, Grafana, or distributed tracing systems.