Site reliability engineer in cloud infrastructure

Описание:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. We are looking for a Senior Site Reliability Engineer (SRE) to join the Compute Node team at Nebius AI Cloud.

The Compute Node team is responsible for building and operating the cluster scheduler and node-level services that run and manage virtual machines across all cloud regions.

• Ensure reliability, availability and performance of compute nodes running VMs
• Analyze and debug Linux systems across user space and kernel space, understanding capabilities, limitations and trade-offs at each layer
• Troubleshoot complex production issues involving CPU, memory, NUMA, cgroups and scheduling
• Work hands-on with virtualization and containerization, primarily using QEMU/KVM and Linux-native technologies
• Design and evolve observability as a core capability of the node layer: metrics, logs, traces, alerts, SLIs and SLOs
• Lead incident response, root-cause analysis, and postmortems, driving long-term reliability improvements
• Collaborate closely with platform, kernel/hypervisor, GPU and infrastructure teams to improve system design and operability

• deep understanding of Linux user space and kernel space knowledge of kernel subsystems (scheduler, memory management, file systems, cgroups, name spaces) clear understanding of system boundaries and constraints at different layers hands-on experience with QEMU/KVM understanding of VM lifecycle, performance characteristics and failure modes practical experience with containers, name spaces and cgroups strong understanding of resource isolation and control ability to reason about complex system failures structured, hypothesis-driven approach to incident analysis clear understanding of the SRE role in system design and operations experience building and operating observability stacks, not just consuming them ability to turn system behavior into actionable reliability signals

• Experience with Kubernetes internals or node-level components;
• Hands-on experience with low-level Linux debugging tools (e.g. perf, eBPF, ftrace, strace, kernel crash dumps);
• Familiarity with large-scale compute or bare-metal platforms;
• Contributions to open-source infrastructure or system software;
• Experience debugging hardware and driver-level issues, including GPUs, NVLink, Infini Band

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams
• Fast moving
• Bold thinking
• Constant growth
• Meaningful impact
• Trust and real ownership
• Opportunity to shape the future of AI