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Senior HPC Cluster Engineer

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Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
22 Nov 2026
Listing views
17
Application actions
1
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AI Summary

The role, at a glance.

Nebius is seeking a senior infrastructure engineer to optimize GPU clusters and InfiniBand fabrics for a large-scale AI cloud platform. The role combines low-level Linux and systems programming with HPC performance analysis, GPU hardware integration, virtualization, and automation. The engineer will troubleshoot complex cluster issues, improve observability and fault remediation, and support hardware through Kubernetes, QEMU, and KVM software stacks. Strong experience with Linux, server architecture, PCIe, networking, and a performance-oriented language such as C/C++, Go, or Python is required. Familiarity with RDMA, RoCE, MPI, NCCL, SDN, and AI frameworks is advantageous.

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 specialized senior role spanning low-level systems software, GPU infrastructure, InfiniBand networking, virtualization, and distributed HPC workloads. Diagnosing performance and reliability problems across hardware and software layers requires substantial independent technical judgment.

Salary analysis

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

Estimated job median (per year)Market rate
$145,000
US market range (per year)$120k–$175k
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 annual USD figures are AI-estimated market compensation for a senior HPC cluster and GPU infrastructure engineer; the yearly cadence is inferred from the senior full-time role and standard professional compensation conventions.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate degraded performance in a multi-node GPU training workload using InfiniBand?

I would first establish a baseline and isolate whether the bottleneck is GPU utilization, CPU scheduling, storage, PCIe topology, or network communication. I would review NCCL and MPI diagnostics, InfiniBand counters, link state, congestion indicators, topology configuration, and host-level metrics. I would then reproduce the issue with focused benchmarks, compare affected and healthy nodes, and validate corrective actions with workload-level measurements.

What factors are important when integrating a new GPU generation into an existing Kubernetes and KVM/QEMU environment?

I would validate driver, firmware, kernel, CUDA, device-plugin, and container-runtime compatibility first. I would also review PCIe topology, IOMMU and VFIO configuration, NUMA placement, GPU passthrough or partitioning requirements, monitoring coverage, and scheduler resource definitions. The rollout should use staged testing with functional, performance, failure-recovery, and regression validation before broad production deployment.

How do RDMA and RoCE differ, and what operational issues would you monitor?

RDMA enables direct memory access between hosts with low CPU overhead and latency, while RoCE carries RDMA traffic over Ethernet. With RoCE, lossless or carefully controlled Ethernet behavior is especially important, so I would monitor priority flow control, ECN, congestion control, packet loss, queue behavior, MTU consistency, and NIC firmware compatibility. For InfiniBand, I would additionally inspect fabric topology, subnet management, link health, and error counters.

Describe an automation approach for proactive detection and remediation of GPU cluster faults.

I would collect host, GPU, fabric, and workload telemetry into a centralized monitoring system and define health signals such as ECC errors, Xid events, thermal limits, link degradation, failed device discovery, and anomalous collective-operation latency. Automated workflows could cordon unhealthy Kubernetes nodes, drain workloads safely, trigger diagnostics, reset recoverable devices, and escalate persistent failures with contextual logs. All remediation actions should be idempotent, audited, and protected by safeguards to prevent broad unintended impact.

How would you optimize a Linux host for a latency-sensitive distributed HPC workload?

I would start by profiling the workload and validating CPU, memory, NUMA, interrupt, PCIe, and network topology. Typical improvements may include CPU affinity and isolation, IRQ balancing or manual interrupt placement, NUMA-aware process pinning, tuned kernel parameters, huge pages where appropriate, and aligned GPU/NIC placement. I would measure each change against representative workload metrics to ensure improvements are real and do not compromise reliability.

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

We’re looking for a Senior HPC Cluster Engineer to join our team and play a key role in the development of our cutting-edge hyperscaler platform. The GPU & InfiniBand team is responsible for enhancing and optimizing the core components of our Cloud platform, with a specific focus on GPU computing, InfiniBand networks, and the KVM/QEMU stack. You’ll work closely with hardware virtualization and device emulation technologies, ensuring high performance and security in multi-GPU, HPC environments. The role involves analyzing, troubleshooting, and improving infrastructure to support new hardware, fine-tuning system performance, and automating fault detection and resolution in a complex system.

In this position, you will be responsible for:

  • Tuning the performance of GPU clusters and InfiniBand networks to ensure optimal operation in HPC and GPU-based environments.

  • Analyzing and troubleshooting the root cause of issues related to GPUs and InfiniBand networks, and proposing corrective actions.

  • Integrating new hardware into the existing infrastructure, including support for new GPU hardware through software stacks like Kubernetes, QEMU, and KVM.

  • Enhancing automation systems for proactive monitoring, detecting, and resolving issues in GPU and InfiniBand environments.

  • Configuring and managing GPU devices and InfiniBand fabrics, ensuring efficient and reliable operation.

We expect you to have:

  • 5+ years of professional experience in system-level software development (focused on performance optimization, low-level programming).

  • 3+ years of hands-on experience with Linux systems (administration, troubleshooting, and performance tuning).

  • In-depth understanding of server architecture, including PCIe devices, NICs, Linux OS/Kernel, and high-performance computing (HPC) systems.

  • Strong proficiency in one or more performance-oriented programming languages (C/C++, Go, Python).

It would be a plus if you have:

  • Experience with GPU end-to-end testing in a cluster environment using InfiniBand networking.

  • Proven track record of analyzing and optimizing the performance of HPC workloads (e.g., simulations, data analysis, AI/ML workloads).

  • Familiarity with RDMA, RoCE, and InfiniBand protocols for high-performance communication.

  • Background in Software-Defined Networking (SDN) and experience with HPC cluster networking.

  • Understanding of QEMU/KVM virtualization and managing virtualized environments.

  • Experience with deep learning frameworks such as PyTorch and TensorFlow, and their integration with HPC systems.

  • Familiarity with collective communication libraries like MPI and NCCL for distributed computing.

We conduct coding interviews as part of the process.

#LI-LH2

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 >

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