Site Reliability Engineer – GPU/HPC Infrastructure; Remote

Position: Site Reliability Engineer – GPU/HPC Infrastructure (Remote – MENA)

This is a fully remote role open to candidates across the MENA region

About Saturn Cloud

Saturn Cloud builds infrastructure for running AI, machine learning, and data workloads platform helps teams develop, deploy, and operate compute-intensive workloads across modern cloud and GPU infrastructure.

We’re looking for a GPU/HPC-focused Site Reliability Engineer based in the MENA region to help operate and troubleshoot the large-scale GPU infrastructure supporting Saturn Cloud Token Factory.

This role is focused on the infrastructure below and around the Kubernetes layer. You’ll serve as a technical escalation point for GPU health, NVIDIA software, high-performance networking, topology, and distributed GPU performance issues affecting production inference workloads.

The ideal candidate comes from GPU infrastructure, HPC, AI infrastructure, neocloud, hyperscaler, or large-scale ML platform operations rather than traditional application support.

We value engineers who actively use modern AI coding agents to improve the speed and quality of their engineering and operational work.

What You’ll Do

Diagnose and resolve production issues affecting large-scale GPU inference infrastructure
Troubleshoot NVIDIA datacenter GPUs, drivers, CUDA compatibility, and GPU container runtimes
Investigate GPU health issues, including Xid errors and hardware or driver failure modes
Diagnose PCIe, NUMA, GPU placement, NVLink, and NVSwitch issues
Troubleshoot multi-GPU and multi-node workloads
Investigate high-performance networking and distributed communication failures
Distinguish application and inference-runtime issues from GPU, fabric, topology, node, driver, or hardware failures
Work with Kubernetes-based GPU workloads and NVIDIA GPU Operator/device plugins
Use production observability and GPU metrics to diagnose reliability and performance issues
Work directly with GPU-cloud and infrastructure providers when incidents require hardware or fabric investigation
Produce clear technical evidence showing where the failure occurs and what the appropriate infrastructure team needs to investigate

What We’re Looking For Core Skills

Hands-on experience using AI coding agents and agentic development tools to accelerate engineering, debugging, automation, and operational workflows
NVIDIA datacenter GPU administration
NVIDIA driver installation, upgrades, and troubleshooting
Strong understanding of CUDA and driver compatibility
Experience with NVML and nvidia-smi
GPU health diagnostics, including Xid errors and common hardware/driver failure modes
Understanding of PCIe topology, NUMA, and GPU placement
Understanding of NVLink and NVSwitch fundamentals
Familiarity with Kubernetes GPU Operator and device plugins
Experience running containerized GPU workloads

High-Performance Networking

You should have strong experience with several of the following:

Infini Band
RDMA
RoCE
NCCL
GPUDirect RDMA
NCCL testing and distributed workload diagnostics
Bandwidth and latency troubleshooting

You should be able to determine whether a production issue originates in the application, GPU, network fabric, topology, node, or another underlying infrastructure layer.

Inference Infrastructure

You don’t need to be an ML researcher, but you should understand how modern inference workloads exercise GPU infrastructure.

vLLM, NVIDIA Dynamo, Triton, or comparable inference runtimes
Tensor and pipeline parallelism
Model loading and GPU memory consumptionKV cache
Continuous batching
GPU and memory utilization
Out-of-memory diagnosis
Multi-GPU and multi-node inference
Basic inference performance analysis, including throughput, latency, and GPU saturation

Additional Skills

Kubernetes troubleshooting sufficient to independently investigate GPU workloads inside a cluster
Prometheus/Grafana and NVIDIA…

Site Reliability Engineer – GPU​/HPC Infrastructure; Remote – MENA)

Site Reliability Engineer – GPU/HPC Infrastructure; Remote – MENA)