HPC Systems Engineer

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Postdoc and Research Support vacancy

HPC Systems Engineer

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Vacancy overview

Job Purpose:

We are seeking a motivated and technically capable High-Performance Computing (HPC) Engineer to join our HPC Infrastructure and Operations team. The role is suitable for early-career engineers with strong Linux fundamentals, practical troubleshooting skills, and a keen interest in GPU computing, distributed systems, containerized workloads, data center networking, and AI infrastructure.

The successful candidate will support the deployment, administration, maintenance, and optimization of large-scale GPU/CPU computing clusters that power cutting-edge Artificial Intelligence (AI), Machine Learning (ML), and scientific research.

Key Responsibilities:

HPC Hardware Operations & Maintenance

Operate, monitor, and maintain large-scale GPU/CPU computing clusters, including NVIDIA/AMD GPU servers, CPU compute nodes, and enterprise storage systems.

Perform hardware diagnostics, preventive maintenance, firmware upgrades, and component replacement for GPUs, CPUs, memory, storage devices, network adapters, and other server components.

Identify and troubleshoot hardware failures, including GPU errors, memory faults, disk failures, thermal issues, and system instability.

Conduct hardware health checks, performance validation, and stress testing to ensure cluster reliability and availability.

Coordinate with hardware vendors and internal infrastructure teams for incident resolution, repairs, and hardware lifecycle management.

HPC System Administration & Cluster Management

Support the deployment, configuration, administration, and maintenance of Linux-based HPC clusters.

Administer and troubleshoot Slurm workload management, including job scheduling, partitions, QoS, resource allocation, job failures, and user access management.

Support distributed storage systems, including Lustre or IBM Storage Scale (GPFS), covering filesystem availability, performance monitoring, client connectivity, and storage-related troubleshooting.

Perform operating system provisioning, patching, driver updates, software installation, and cluster configuration management.

Monitor cluster health, system performance, resource utilization, and service availability.

GPU Computing & AI Workload Support

Provide technical support to researchers running AI/ML training, inference, and scientific computing workloads across GPU/CPU clusters.

Diagnose and resolve job execution failures related to Slurm, CUDA, NVIDIA drivers, NCCL, MPI, PyTorch, distributed training frameworks, and system dependencies.

Troubleshoot GPU memory issues, resource allocation problems, multi-GPU/multi-node communication failures, and performance bottlenecks.

Assist researchers with job submission, resource optimization, environment configuration, and best practices for efficient cluster utilization.

Support the validation and benchmarking of GPU/CPU compute nodes and distributed AI workloads.

Automation, Monitoring & Operational Support

Develop and maintain scripts and automation tools using Python, Bash, or other programming languages to streamline HPC administration and operational tasks.

Support automated system provisioning, configuration management, monitoring, and health-check processes.

Maintain technical documentation, troubleshooting guides, operational procedures, and incident reports.

Participate in incident response, root-cause analysis, preventive maintenance, and continuous infrastructure improvement.

Collaborate with researchers, HPC engineers, IT teams, and technology vendors to ensure stable and efficient HPC services.

HPC Containers & Data Center Networking

Build, maintain, and support HPC container environments using Apptainer (Singularity), including definition files, image builds, GPU and MPI integration, and secure execution of containerized jobs under Slurm.

Support the deployment and operation of Kubernetes-based platforms for AI/ML services, inference, and research workflows, including GPU scheduling (NVIDIA GPU Operator / device plugin) and integration with HPC storage.

Maintain container registries and curated base images (e.g., CUDA, PyTorch, MPI, NGC) to provide reproducible, secure, and up-to-date software environments for researchers.

Operate, monitor, and troubleshoot data center network switches (Ethernet and InfiniBand), including port configuration, VLANs, link aggregation, firmware upgrades, cabling/optics faults, and fabric health checks.

Support high-performance cluster fabrics (InfiniBand, RoCE/RDMA) and leaf-spine topologies, working with network teams and vendors to resolve latency, congestion, and link-error issues affecting distributed workloads.

Other Duties:

Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.

Qualifications and Criteria:

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, Information Technology, or a related technical discipline.

Master’s degree in a relevant field, or equivalent practical experience in Linux systems, HPC infrastructure, or distributed computing will be preferred.

Essential:

Strong understanding of Linux operating systems, command-line tools, system services, processes, permissions, and troubleshooting methodologies.

3-5 years of practical experience with Linux server administration, hardware diagnostics, or infrastructure operations.

Understanding of computer hardware architecture, including CPUs, GPUs, memory, storage, and networking components.

Familiarity with scripting languages such as Python and Bash for system administration and automation.

Fundamental knowledge of TCP/IP networking, SSH, DNS, network interfaces, and system connectivity.

Working knowledge of container technologies such as Apptainer (Singularity), Docker, or Podman, including building, running, and troubleshooting container images.

Hands-on exposure to data center network switches, including Ethernet switching concepts (VLANs, LACP, MTU/jumbo frames, LLDP) and switch CLI configuration and troubleshooting.

Ability to investigate technical problems using system logs, monitoring tools, and structured troubleshooting approaches.

Strong analytical, problem-solving, documentation, and communication skills.

Ability to work collaboratively with researchers, engineers, and technical support teams.

Preferred:

GPU Computing & AI Infrastructure

Familiarity with NVIDIA GPU architecture, drivers, CUDA, and GPU monitoring tools such as nvidia-smi and NVIDIA DCGM.

Exposure to PyTorch, NCCL, or distributed AI/ML training workloads.

Understanding multi-GPU and multi-node computing environments.

Containers & Orchestration

Hands-on experience with Kubernetes administration (deployments, Helm, namespaces, RBAC, persistent storage) in GPU-enabled environments.

Familiarity with the NVIDIA Container Toolkit, NVIDIA GPU Operator, and running containerized MPI/NCCL workloads via Apptainer or Pyxis/Enroot under Slurm.

Exposure to container registries (e.g., Harbor), CI/CD pipelines, and configuration/infrastructure-as-code tools such as Ansible or Terraform.

Data Center Networking

Experience with InfiniBand fabrics (e.g., NVIDIA Quantum switches, UFM, Subnet Manager, ibdiagnet) and RDMA/RoCE (PFC, ECN) for GPU clusters.

Familiarity with data center switch platforms such as NVIDIA Spectrum (Cumulus Linux), Arista EOS, Cisco NX-OS, or Juniper, and leaf-spine/BGP-EVPN architectures.

Ability to monitor and benchmark network performance using tools such as SNMP, Prometheus/Grafana, perftest (ib_write_bw), and iperf.

Application open

Employment typeFull-Time

CategoryPostdoc and Research Support

LocationAbu Dhabi, UAE

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HPC Systems Engineer

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