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    Job · 21709342

    Nebius logo

    Lead Site Reliability engineer - Network infrastructure - APAC

    Nebius · Remote · remote · Posted 12d ago

    Not disclosed7+ yrsfulltimeSourced fromGreenhouse

    About a minute. You'll make an account on the way through.

    About this role

    About Nebius:

    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.

    Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

    The Role

    We're looking for a Lead Site Reliability Engineer to build and run the fundamental part of Nebius in APAC: the Network, the infrastructure everything else depends on. We are at the early stage of our APAC expansion, so this is a hands-on role. You will define reliability targets, write automation and handle incidents yourself, with a high degree of independence from the European teams. As the region grows, you will build and lead the local SRE team.

    Your responsibilities will include:

    Define and own reliability goals for network services and critical paths in the region (SLIs/SLOs, availability targets, error budgets where it makes sense)

    Drive reliability improvements across the whole APAC network: not only services, but also site readiness, inter-site connectivity (DCI), and operational standards for new data centers

    Own incident response for your areas, lead investigations/postmortems, and turn failures into durable fixes (not repeated firefighting); set up on-call and incident management practices for the region

    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 in APAC and Europe to embed operability into designs and keep operations practical and fast

    Hire, onboard and mentor the first SREs in the region and set the engineering bar for the team

    Coordinate with European teams on handoffs and coverage across time zones

    We expect you to have:

    7+ years of experience in SRE / infrastructure engineering, including experience leading a team or acting as a technical lead with broad ownership of production systems

    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

    Ability to work independently in an early-stage environment and to build processes and a team from scratch

    Willingness to stay hands-on while leading and growing a team

    It will be an added bonus if you 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 (routing/flow telemetry, regression detection at scale)

    Knowledge of the APAC network landscape (carriers, IX, regional connectivity and regulatory specifics)

    Benefits & Perks:

    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

    What's it like to work at Nebius:

    Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI 

    Equal Opportunity Statement:

    Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.

    Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. 

    If you need accommodations during the application process, please let us know.

    Sourced from greenhouse

    Skills

    PythonLinuxCi/Cd

    About Nebius

    Nebius logo

    Nebius

    Amsterdam

    NL

    About

    Nebius AI is an AI cloud infrastructure company that provides full-stack computing services and platforms. Nebius AI is an AI-centric public cloud platform, launched in late 2023 and specifically crafted to serve AI models from ML training to inferencing at a scale. Our mission is to enable ML practitioners to concentrate on their core job while DevOps, MLOps and infrastructure related tasks are on us. Our goal is to build an ML-specific cloud platform covering the whole ML lifecycle from A to Z: from data preparation and data labeling to ML training and inference.

    Industries

    AI InfrastructureCloud InfrastructureGPUIaaSPaaS

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