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Software Engineer in Network Infrastructure
- Remote from
- Europe
- Salary
- Undisclosed
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Entry-Level, Junior
- Published
- Apply before
- 6 Dec 2026
- Listing views
- 30
- Application actions
- 3
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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 Software Engineer to build software that makes network operations safe, scalable, and boring — even as we launch new data centers and expand fast. This is not a “write scripts for configs” role: you’ll build the tooling and services that sit between the network core (switches/ports/VLANs, traffic processors) and the cloud platform on top, using open source where it fits and building the missing pieces where it doesn’t.
Your responsibilities will include:
- Build and maintain services and tooling that automate the network lifecycle: day-0 provisioning, day-N changes, drift detection, and operational verification
- Make network changes safe and transparent: CI/CD workflows, diff/review tooling, staged rollouts/rollbacks, audit trails, and guardrails
- Develop observability systems that scale across many sites: telemetry pipelines, signal quality, and tooling that shortens incident investigations
- Close “last mile” gaps between the network and the platform: integrate source-of-truth data, expose APIs, and build reliable automation around it
- Collaborate closely with network engineers and SREs to turn real operational pain into product-quality tooling
We expect you to have:
- 5+ years of professional software engineering experience (or equivalent practical background)
- Strong coding skills and ownership mindset: you can ship and operate reliable services
- Proficiency in Go or readiness to switch; Python is also welcome (other languages can be useful for OSS debugging/fixes)
- You don’t have to be Network expert but we expect you to have interest in infrastructure and/or network
It will be an added bonus if you have:
- Background in networking (ex-network engineer, CCNP/education, DC networking exposure) or strong interest and proven ability to learn fast
- Experience building automation/infra tooling: CI/CD, IaC, testing/staging environments, or “network-as-code” style workflows
- Low-level networking / datapath experience: eBPF/XDP, DPDK, kernel networking, traffic processing systems
- Experience designing high-load services and observability platforms (metrics/logs/traces, alerting, regression detection)
- Contributions to open source or experience extending/debugging OSS components in production environments
We expect Engineers to:
- Manage large-scale projects involving multiple stakeholders
- Break down complex tasks and guide both their own work and that of more junior colleagues
- Be experts in specific technologies and write high-quality code that can serve as a reference
- Assess task priority and focus on high-impact work, avoiding low-value efforts
- Have strong architectural thinking and contribute to system design
- Be involved in hiring and actively contribute to interviews
- Be willing to share knowledge and mentor others
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would use a source-of-truth-driven workflow with pull-request review, automated validation, policy checks, and generated configuration diffs. Changes would progress through staging and canary rollouts with health gates, audit trails, and automated rollback paths. The design should make both intended state and observed device state visible.
I would regularly collect normalized device state and compare it with the approved desired state from the source of truth. Drift events should be classified by severity, correlated with recent changes, and surfaced through dashboards and alerts. For safe, well-understood cases, controlled remediation can be automated; otherwise, the system should create an actionable investigation record.
The platform needs reliable collection, normalization, cardinality control, retention policies, and clear service-level signals. I would combine metrics, logs, traces, topology context, and deployment metadata so operators can correlate symptoms with configuration or platform changes. Data quality and cost controls are essential as the number of sites and devices grows.
I would start with operational pain points measured through incident frequency, manual effort, risk, and customer impact. Together, we would define outcomes, interfaces, operational ownership, and rollout criteria, then prioritize work that reduces risk or removes repeated manual steps. Regular feedback from incident reviews and users would guide iteration.
I treat operability as part of delivery: define SLOs, dashboards, alert thresholds, capacity assumptions, runbooks, and rollback procedures before broad rollout. After release, I monitor latency, errors, saturation, and dependency behavior, then use production feedback to improve reliability and simplify operations. I also document key decisions and share knowledge with the team.
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