• Bridge ingestion and use. Design the pipelines and metadata that turn ingested data into something findable and consumable — catalogs, schemas, and access layers that match how training jobs and simulations actually read, not just where bytes sit.
• Make data traceable. Build lineage and provenance so any dataset, checkpoint, or result can be traced back to its inputs and transformations. Reproducibility is a first-class requirement here, not a retrofit.
• Tune for the workload. Optimise parallel filesystems (Lustre, GPFS) and object storage for the concurrency, small-file, and large-checkpoint patterns of distributed GPU training and HPC simulation.
• Operate at scale, safely. Design and run multi-petabyte storage with the integrity and availability scientific work depends on — erasure coding, redundancy, hot-to-archival tiering.
• Automate everything. Deploy and scale storage and data services as code. Snowflake infrastructure doesn't survive at this scale.
• Make it observable. Instrument storage health, capacity trends, and pipeline performance so problems surface before users feel them.
• Translate. Turn real access patterns from domain scientists and ML engineers into technical requirements — and push back when a request would quietly break something downstream.

For a project in the weather and climate domain, aimed at understanding and mitigating the impact of climate change, an opening for two years is available.

The initial two-year contract could potentially be extended or even become permanent.

• A technical degree (CS, engineering) or equivalent experience that demonstrates the same depth.
• Solid storage grounding: filesystems (block and object), performance tuning, redundancy (RAID, erasure coding).
• Python, and comfort automating infrastructure (Ansible, Terraform, or similar).
• A working understanding of how ML and scientific workloads consume data — billions of small files, large checkpoints, sharding — and why naive layouts fall over.
• A point of view on data lineage, provenance, or reproducibility — and ideally tooling you've used to enforce it.

What helps you stand out

• Hands-on parallel filesystems (Lustre, Spectrum Scale/GPFS) or distributed storage (Ceph, VAST).
• Scientific data formats — HDF5, Zarr, Parquet — and opinions on when each earns its place.
• Object storage (S3) interfaced with ML frameworks (PyTorch, TensorFlow).
• Orchestration (Kubernetes, Argo) and data-movement tooling.
• Data versioning / cataloguing (e.g. DVC, lakeFS, a metadata catalog) and familiarity with FAIR data principles.
• CI/CD and provisioning: GitLab CI, HashiCorp Vault, MAAS.

We don't expect every box ticked. Depth in storage or data engineering, plus the curiosity to grow into the other, matters more than a complete checklist.

What you get

• Hardware and scale you won't find in enterprise IT — and problems with no vendor playbook.
• Work that directly enables published science and frontier-scale model training.
• Room to shape how data is managed, not just maintained, in an environment that takes it seriously.

Our core values as guiding principles:

• Curiosity: You enjoy learning and understanding systems deeply
• Openness: You collaborate effectively and value different perspectives
• Courage: You are willing to tackle difficult or unfamiliar problems
• Supportive: You help colleagues and users succeed
• Integrity: You act responsibly, reliably, and transparently