We’re looking for a Network Software Engineer (NetSWE) 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 Staff 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
Pay Transparency
We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.
Base Compensation Range
$179,500—$224,300 USD
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
Published on: 8/6/2026

Nebius
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.
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