Responsibilities
- Own reliability for large-scale Kubernetes and GPU clusters, including incident response, root-cause analysis, and corrective actions.
- Manage CPU and GPU cluster and node provisioning, upgrades, scaling, repair, replacement, and decommissioning.
- Build safe rollout, rollback, and validation mechanisms for cluster-level changes.
- Improve workload placement, utilization, isolation, availability, quotas, priorities, preemption, affinity, autoscaling, and accelerator-aware scheduling.
- Own compute capacity planning, resource forecasting, performance tuning, and infrastructure cost balancing across clusters and failure domains.
- Define SLOs and error budgets and improve monitoring, alerting, runbooks, production-readiness reviews, and failure recovery.
- Build automation and automated remediation to reduce operational toil and improve deployment safety.
- Partner with workload owners and infrastructure teams on platform standards, design reviews, and operational practices.
Requirements
- Bachelor’s degree in Computer Science, 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, including troubleshooting clusters, nodes, pods, controllers, and workload scheduling.
- Experience managing CPU and GPU compute resources across 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 experience with large multi-cluster or multi-region Kubernetes fleets, GPU clusters, and AI infrastructure at scale.
- Preferred knowledge of Kubernetes architecture and internals, including the API server, etcd, scheduler, controller manager, kubelet, admission control, custom controllers, and operators.
- Preferred experience with distributed AI/ML training or inference workloads, infrastructure-as-code, configuration management, Kubernetes deployment tooling, SLOs, capacity models, failure-domain planning, observability, automated remediation, and workload recovery.
- Strong troubleshooting, written and verbal communication, and ownership during high-impact incidents.
Benefits
- Rotational on-call schedule providing continuous coverage for critical compute infrastructure.
- Work across multiple time zones with rigorous 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.