Senior Scientific Computing Lead & HPC/GPU Architect (Hybrid Madrid)

Spanish National Cancer Research Centre (CNIO); Madrid; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

CNIO, one of Europe’s leading cancer research centres, seeks a Senior Scientific Computing Engineer to join the IT, Scientific Computing and AI area in Madrid. You will own the infrastructure behind our genomics pipelines and AI models, shaping the future of scientific computing at the centre.

You will manage and evolve the Slurm HPC cluster, build GPU/AI infrastructure, and collaborate with researchers to create robust, reproducible pipelines.

Qualifications

• Experience running Linux HPC environments in production (Slurm or similar).
• Automation with Ansible and Python; containers such as Apptainer/Docker; workflow managers Nextflow or Snakemake.
• Ability to set direction for scientific computing rather than simply maintain systems.
• Bonus: Ceph, Kubernetes, GPU stacks, bioinformatics background.
• Spanish not required.

Responsibilities

• Shape the roadmap for scientific computing at the centre and drive it forward.
• Own and evolve our HPC cluster (Slurm): scheduling, monitoring, capacity planning, user support.
• Build out our GPU / AI infrastructure as the centre's ML workloads grow.
• Work with researchers to turn analyses into robust, reproducible pipelines and tools.
• Optional: co-author research papers.

Skills

Linux HPC environments

Automation (Ansible, Python)

Containers (Apptainer, Docker)

Workflow management (Nextflow, Snakem​

Tools

Slurm

Apptainer

Docker

Nextflow

Snakemake

Ceph

Kubernetes

Job description

CNIO, one of Europe’s leading cancer research centres, seeks a Senior Scientific Computing Engineer to join the IT, Scientific Computing and AI area in Madrid. You will own the infrastructure behind our genomics pipelines and AI models, shaping the future of scientific computing at the centre.

You will manage and evolve the Slurm HPC cluster, build GPU/AI infrastructure, and collaborate with researchers to create robust, reproducible pipelines.