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Senior Systems HPC Engineer

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Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
27 Nov 2026
Listing views
31
Application actions
2
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AI Summary

The role, at a glance.

Nebius seeks a senior systems engineer to optimize performance across large-scale GPU clusters powering an AI cloud platform. The role spans hardware, Linux system software, high-speed networking, virtualization, and distributed communication layers such as MPI and NCCL. Key work includes diagnosing training and inference bottlenecks, qualifying cluster hardware, and integrating tuning approaches across the software stack. The engineer will partner with infrastructure teams, software developers, customers, and vendors including NVIDIA, Mellanox, and Intel. Candidates need substantial low-level performance engineering and Linux administration experience, plus proficiency in C/C++, Go, or Python.

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 systems role requiring deep expertise across GPU hardware, Linux internals, networking, virtualization, and distributed AI workloads. Engineers must independently investigate complex cross-stack performance issues and communicate findings to internal teams, customers, and hardware vendors.

Salary analysis

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

Estimated job median (per year)Market rate
$190,000
US market range (per year)$160k–$230k
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 cadence and USD market estimate are inferred for a senior HPC systems engineering role supporting large-scale GPU infrastructure.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you investigate degraded throughput in a GPU training cluster?

I would first establish a reproducible workload and baseline, then collect metrics across GPU utilization, CPU, memory, storage, PCIe, and network layers. I would correlate framework-level profiling with NCCL or MPI traces and system telemetry to isolate whether the bottleneck is compute, communication, I/O, or configuration related. After testing a targeted change, I would validate the result against the baseline and document the operational recommendation.

What factors affect NCCL performance over InfiniBand or RoCE?

Important factors include topology awareness, NIC and GPU affinity, PCIe or NVLink paths, MTU settings, queue-pair configuration, congestion control, driver and firmware compatibility, and collective algorithm selection. I would also verify that the cluster fabric is healthy and use NCCL tests to distinguish communication-layer issues from workload behavior.

Describe your approach to qualifying new GPU cluster hardware.

I would define measurable acceptance criteria for compute, memory, storage, network bandwidth, latency, resilience, and thermal behavior. I would run component diagnostics and representative end-to-end AI workloads, compare results to expected baselines, and investigate deviations with vendors and internal engineering teams. The outcome would be a repeatable qualification suite and clear pass or remediation criteria.

How have you used Linux tools to diagnose a system-level performance issue?

I use tools such as perf, eBPF-based tracing, ftrace, sar, iostat, vmstat, ethtool, and kernel logs depending on the suspected layer. I begin with system-wide symptoms, narrow the scope through correlated metrics, and then capture targeted traces to identify contention, interrupts, scheduling issues, I/O waits, or network errors. I validate conclusions through controlled experiments rather than relying on a single metric.

How would you communicate a complex performance escalation to a customer and an internal hardware vendor?

I would provide a concise statement of impact, environment, reproduction conditions, measured evidence, and the current hypothesis. I would separate confirmed facts from open questions, share relevant logs and benchmark results, and propose specific next diagnostic steps with owners and timelines. This keeps technical stakeholders aligned while giving the customer a clear, actionable status update.

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.

We are looking for a Senior Systems HPC Engineer to play a key role in building our hyperscaler platform, working across its core components while analyzing and optimizing the performance of large-scale GPU clusters at the intersection of hardware and software.

You will operate across the full stack—from hardware and system software to networking (InfiniBand/RoCE), virtualization (KVM/QEMU), and distributed communication layers (e.g., MPI, NCCL).

In this role you will

  • Focus on understanding system behavior across multiple layers, identifying performance bottlenecks, and driving improvements that shape how our clusters are built, operated, tuned, and validated.
  • Investigate and troubleshoot performance issues of GPU cluster under real workloads (training and inference)
  • Evaluate and integrate new hardware, system configurations and tuning approaches through software stack
  • Support complex performance-related escalations from internal teams and customers
  • Work closely with infrastructure, software engineering and hardware vendor teams (e.g. NVIDIA, Mellanox, Intel)
  • Contribute to hardware and cluster qualification (acceptance), ensuring systems meet performance expectations

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).

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 >

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.

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