Site reliability engineer in network infrastructure

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. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

• Define and own reliability goals for network services and critical paths (SLIs/SLOs, availability targets, error budgets where it makes sense)
• Drive reliability improvements across the whole network: not only services, but also site readiness, inter-site connectivity (DCI), and operational standards
• Own incident response for your areas, lead investigations/postmortems, and turn failures into durable fixes (not repeated firefighting)
• Build and evolve observability: actionable metrics/logs/traces, alerting, and faster debug loops during and after incidents
• Design safer change workflows: automation, CI/CD, test/staging environments, canarying, rollbacks, and auditability for network changes
• Work closely with network engineers and platform teams to embed operability into designs and keep operations practical and fast

• Strong production Linux fundamentals and a structured approach to debugging complex systems
• Solid understanding of networking basics and how real networks fail (control plane vs data plane, latency/loss, failure domains, etc.)
• Hands-on experience operating high-availability systems and improving them over time (not just “keeping lights on”)
• Ability to write and maintain software/automation (Go is common for us; Python is also welcome)
• Experience with modern infrastructure tooling (e.g., IaC, CI/CD, container platforms) and comfort automating operational workflows
• Будет плюсом: Experience with high-throughput traffic processing: load balancers, tunneling/decap, NAT64, or similar datapath-heavy systems; Low-level networking performance/debug background (eBPF/XDP, DPDK, perf/ftrace, kernel networking internals); Experience building network-safe delivery pipelines (testing labs, staged rollouts, automated verification, drift detection); Background with large-scale network observability/telemetry (e.g., routing/flow telemetry, regression detection at scale)

• 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