Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Systems HPC Engineer
Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.
- Remote from
- Europe
- Salary
- Undisclosed
- Department
- DevOps & Infrastructure
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 27 Nov 2026
- Listing views
- 31
- Application actions
- 2
Make your next move.
Prepare your resume, explore your fit, and draft a cover letter for this opportunity.
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.
Pace & Pressure
5/5Autonomy Level
4/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Sample interview questions
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.
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.
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.
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.
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.
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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.
Apply now.
Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information.
Continue on the employer website
Protect your personal information and never pay to secure an interview or job offer. .
