Senior Systems HPC Engineer
Key Skills
Job Description
Why work at Nebius
Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.
Where we work
Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with R&D hubs across Europe, North America, and Israel. The team of over 1400 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI R&D team.
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.
What we offer
- Competitive salary and comprehensive benefits package.
- Opportunities for professional growth within Nebius.
- Flexible working arrangements.
- A dynamic and collaborative work environment that values initiative and innovation.
We’re growing and expanding our products every day. If you’re up to the challenge and are excited about AI and ML as much as we are, join us!
Core Responsibilities
You will analyze and optimize the performance of large-scale GPU clusters by identifying bottlenecks across hardware and software layers. Additionally, you will support performance-related escalations and contribute to hardware qualification and cluster validation.
Requirements
Candidates must have at least 5 years of professional experience in system-level software development and 3 years of hands-on Linux administration and performance tuning. Proficiency in performance-oriented languages like C, C++, Go, or Python is required, along with deep knowledge of server architecture and HPC systems.
Benefits
- Competitive salary
- Comprehensive benefits package
- Professional growth opportunities
- Flexible working arrangements
About Nebius
Industry: Technology, Information and Internet
Company size: 1,001-5,000 employees
The Nebius AI Cloud brings powerful full-stack infrastructure for AI developers and practitioners across startups, enterprises and science institutes to build and deploy generative AI applications and rapidly deliver scientific breakthroughs by training and running ML models within a secure, high-performance, and cost-optimized cloud environment.