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System Engineer (Compute Node)

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Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
Apply before
3 Dec 2026
Listing views
22
Application actions
1
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Opportunity details

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

The Compute Node team builds services for managing Virtual Machines running on GPU servers, integrating with disk management, virtual and InfiniBand networks. The team is also responsible for developing the Virtual Machine Scheduler that operates across clusters with thousands of servers and tens of thousands of GPUs, spanning multiple data centers in several regions.

We expect you to have:

1. Containerization & Orchestration

  • Experience with Kubernetes not only for deployment but for development is highly valued.

2. Virtualization

  • KubeVirt and QEMU/KVM familiarity is a high plus.

3. Linux & System-level Understanding

  • Solid knowledge of internal OS architecture, performance considerations, process isolation and resource management.

  • Knowledge of POSIX, sysfs, system calls, and file systems.

  • Familiarity with server architecture, PCIe devices, NICs, and kernel drivers.

4. Hardware Acceleration

  • Experience or strong interest in working with GPUs, DPUs, or ARM architectures is desirable.

  • Familiarity with the NVIDIA DOCA Software Framework will be an advantage.

5. Go, C++ Proficiency

  • Knowledge of at least one of these languages, ready to learn or work with the others as needed.

  • Strong grasp of concurrency, debugging, and profiling techniques.

We conduct coding interviews as part of the process.

#LI-JS1

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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

AI Summary

The role, at a glance.

Nebius is seeking a System Engineer to build and operate compute-node services for GPU-backed virtual machines in a large-scale AI cloud platform. The role focuses on VM lifecycle management, scheduling across large clusters, storage and network integration, and performance-sensitive Linux systems work. Candidates need strong Kubernetes, virtualization, Linux internals, concurrency, debugging, and Go or C++ capabilities. Familiarity with GPU infrastructure, InfiniBand, server hardware, and NVIDIA DOCA is advantageous. The position is remote across Europe and offers significant ownership in a fast-moving infrastructure engineering environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a highly technical systems role involving distributed scheduling, GPU infrastructure, virtualization, and low-level Linux behavior at substantial scale. Success requires deep debugging ability and sound engineering decisions across hardware and software boundaries.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$150,000
US market range (per year)$120k–$190k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The yearly cadence and USD market estimate are inferred for a senior-level cloud infrastructure and systems engineering role; actual compensation will vary by European hiring country, level, and equity structure.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate intermittent VM startup failures on GPU-enabled Kubernetes nodes?

I would first correlate scheduler, Kubernetes, hypervisor, container runtime, and node kernel logs using timestamps and node identifiers. I would then validate GPU device discovery, driver compatibility, resource requests, CNI and storage dependencies, and compare healthy and failing nodes. Finally, I would reproduce the issue where possible, add targeted metrics or tracing, and implement a fix with regression coverage and operational alerts.

What are the main trade-offs when designing a scheduler for GPU workloads across multiple clusters?

The scheduler must balance utilization, locality, fairness, queue latency, fault domains, and workload constraints such as GPU type, topology, network bandwidth, and storage access. Packing improves utilization but can increase fragmentation or create hotspots, while spreading improves resilience but may increase communication cost. I would make policy decisions observable and configurable, with clear admission, preemption, and recovery behavior.

Explain how you would debug a performance regression in a concurrent Go service.

I would establish a reproducible baseline and use metrics, traces, CPU profiles, heap profiles, and goroutine dumps to identify whether the bottleneck is CPU, allocation pressure, lock contention, I/O, or an external dependency. I would inspect recent changes and compare workload characteristics before narrowing to a minimal reproducer. After fixing the root cause, I would add benchmarks and monitoring to prevent recurrence.

What is the role of process isolation and resource controls in a multi-tenant compute platform?

They protect tenant workloads from interfering with one another and help ensure predictable performance and security. I would use mechanisms such as namespaces, cgroups, device access controls, network policies, quotas, and carefully scoped privileges. For GPU workloads, I would also validate accelerator allocation, driver isolation, and resource accounting.

How would you approach integrating KubeVirt with disk and network provisioning systems?

I would define clear lifecycle contracts between VM creation, storage attachment, network configuration, readiness checks, and cleanup. The implementation should be idempotent, resilient to partial failures, and able to reconcile desired versus actual state. I would expose structured events and metrics so operators can diagnose failures across the VM, storage, and networking layers.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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