Responsibilities
- Operate and improve large-scale Kubernetes and GPU clusters, including control planes, nodes, container runtimes, scheduling, and host-level dependencies.
- Participate in rotational on-call coverage and lead incident response, troubleshooting, root-cause analysis, and corrective actions.
- Manage CPU and GPU cluster and node provisioning, upgrades, scaling, repair, replacement, validation, rollback, and decommissioning.
- Improve heterogeneous resource scheduling, utilization, isolation, quotas, priorities, preemption, affinity, autoscaling, and accelerator-aware placement.
- Own compute capacity planning, resource forecasting, performance tuning, reliability, utilization, and infrastructure cost across clusters and failure domains.
- Define SLOs and error budgets and improve monitoring, alerting, runbooks, production-readiness reviews, and failure recovery.
- Build automation, tooling, and automated remediation while partnering with workload owners and infrastructure teams on platform standards and operational practices.
Requirements
- Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
- At least 3 years of experience in Site Reliability Engineering, Production Engineering, Systems Engineering, or a similar infrastructure role.
- Strong hands-on experience operating Kubernetes in production and troubleshooting clusters, nodes, pods, controllers, and workload scheduling.
- Experience managing CPU and GPU compute resources, including capacity, allocation, isolation, utilization, and failure recovery.
- Strong knowledge of Linux systems, container runtimes, cgroups, namespaces, networking fundamentals, and host-level performance troubleshooting.
- Proficiency in Go, Python, Bash, or another programming language used for automation and infrastructure tooling.
- Preferred qualifications include experience with large multi-cluster or multi-region Kubernetes fleets, GPU clusters, AI infrastructure, Kubernetes internals, heterogeneous resource scheduling, distributed AI/ML workloads, infrastructure-as-code, configuration management, and Kubernetes deployment tooling such as Terraform, Ansible, or Helm.
- Preferred candidates demonstrate experience with AI infrastructure reliability mechanisms, SLOs, capacity models, failure-domain planning, observability, automated remediation, workload recovery, and complex incident troubleshooting.
Benefits
- The role includes a rotational on-call schedule providing continuous coverage for critical compute infrastructure.
- The team works across multiple time zones and follows formal change management and post-incident review practices.
About ByteDance
ByteDance is a global incubator of platforms at the cutting edge of commerce, content, entertainment and enterprise services - over 2.5bn people interact with ByteDance products including TikTok. Creation is the core of ByteDance's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. At ByteDance, we create together and grow together. That's how we drive impact - for ourselves, our company, and the users we serve. We are committed to building a safe, healthy and positive online environment for all our users. We have over 110,000 employees based in more than 30 countries globally. Join us.