About the position
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
- Engineer hyper-scale cluster management: Enhance Kubernetes-based cluster platforms to deliver exceptional performance, scalability, and resilience-powering resource management across ByteDance's massive global infrastructure.
- Innovate on core scheduling capabilities: Design and maintain a truly unified scheduling that powers diverse workloads (Containers & VMs, online services, offline computing, AI/ML, CPU/GPU workloads, etc) in a massive-scale resource pool.
- Develop an intelligent scheduling system: Leverage AI models to optimize workload performance and resource utilization across heterogeneous resources-including CPU, GPU, memory, network, and power across global data centers.
- Lead Infrastructure for Next-Gen ML Workloads: Design and drive the evolution of compute platforms purpose-built for fast, reliable, and cost-effective ML and LLM training/inference.
- Deliver Quality and Innovation: Write high-quality, maintainable code, and stay at the forefront of open-source and research advancements in AI, ML, systems, and Serverless technologies.
