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Senior HPC Engineer, GPU Compute

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Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
22 Nov 2026
Listing views
18
Application actions
2
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AI Summary

The role, at a glance.

Nebius is seeking a senior HPC Cluster Engineer to optimize GPU compute clusters and InfiniBand networking for its AI cloud platform. The role focuses on performance tuning, root-cause analysis, hardware integration, and automation for reliable multi-GPU environments. Candidates need substantial low-level systems development and Linux performance-tuning experience, along with knowledge of server architecture, PCIe, NICs, and HPC systems. Valuable additional expertise includes RDMA, RoCE, InfiniBand, Kubernetes, QEMU/KVM, MPI, NCCL, and distributed AI workloads. The interview process includes coding interviews, and the position offers remote work across Europe.

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 infrastructure role requiring deep expertise across Linux internals, GPU hardware, high-performance networking, virtualization, and distributed computing. The engineer must diagnose complex production issues and deliver measurable performance and reliability improvements in a hyperscale environment.

Salary analysis

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

Estimated job median (per year)Market rate
$175,000
US market range (per year)$150k–$210k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Nebius does not disclose employer compensation. The annual USD figures are AI estimates for a senior HPC/GPU infrastructure engineer and use an inferred yearly cadence based on the senior full-time role and US market benchmark context.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate a sudden reduction in multi-node GPU training throughput on an InfiniBand cluster?

I would first establish a baseline and isolate whether the bottleneck is compute, GPU interconnect, host CPU, storage, or network communication. I would inspect GPU utilization and errors, NCCL and MPI logs, InfiniBand counters, topology, link speeds, RDMA configuration, CPU affinity, and PCIe placement. I would then run focused microbenchmarks such as NCCL tests and bandwidth or latency tests, compare affected nodes with healthy nodes, and validate the corrective action using workload-level metrics.

What factors do you consider when tuning GPU workloads on a Linux HPC cluster?

I consider GPU and driver versions, CUDA compatibility, NUMA topology, CPU pinning, PCIe locality, memory limits, power and thermal behavior, storage throughput, network configuration, and collective communication settings. I also verify scheduler placement, container runtime settings, and whether the workload has an imbalance across GPUs or nodes. Tuning should be measured against a repeatable benchmark and validated for stability, not only peak throughput.

Explain the importance of RDMA and InfiniBand for distributed AI or HPC workloads.

RDMA enables direct memory access across hosts with low CPU overhead and low latency, which is important when workers exchange large tensors or simulation data frequently. InfiniBand provides high bandwidth and low-latency transport suitable for these patterns. Together with libraries such as NCCL and MPI, they reduce communication overhead and help distributed jobs scale efficiently across many GPUs and nodes.

How would you safely introduce new GPU hardware into an existing Kubernetes and QEMU/KVM-based platform?

I would begin with hardware and firmware compatibility validation in a controlled test environment, including drivers, kernel versions, device plugins, virtualization support, and telemetry. I would create automated functional, performance, failure-recovery, and isolation tests before a staged rollout. During deployment, I would use canary nodes, monitor error rates and performance regressions, document rollback procedures, and expand only after the platform meets defined reliability and throughput criteria.

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

I would collect hardware, driver, GPU, and fabric telemetry into centralized monitoring with thresholds and anomaly detection based on normal cluster behavior. Automated health checks could identify issues such as GPU Xid errors, ECC events, degraded links, port errors, or abnormal latency, then quarantine affected nodes from scheduling and create actionable incidents. Remediation should be carefully bounded, auditable, and paired with escalation when automated recovery cannot establish a safe healthy state.

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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