About
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.
Responsibilities
•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
Requirements
•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
Nice to have
•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)
Conditions
•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
