PpbCSCS /b develops and operates a high-performance computing and data research infrastructure that supports world‑class science in Switzerland. Its user laboratory is available to domestic and international researchers in academia, industry, and the business sector. The centre is operated by ETH Zurich and has offices at its data centre in Lugano and in Zurich. /p pFor this position the work location is either Lugano or Zürich. The contract is for two years. /p h3Project background /h3 pStoring petabytes is the easy part. The hard part is everything between the moment data lands on disk and the moment a researcher – or a training job – can actually trust it, find it, and use it. /p pOur parallel filesystems and object stores already move data fast. What they don't do on their own is tell a scientist where a dataset came from, which transformations produced it, whether it's the version that backed last quarter's published result, or how to feed it to a DataLoader without saturating the I/O subsystem. /p pThat gap – between raw bytes and usable, traceable, reproducible data – is where this role lives. /p pYou’ll work at both ends: the storage layer (throughput, integrity, tiering at multi‑petabyte scale) and the data layer above it (lineage, provenance, discoverability, access patterns). If you’ve ever been annoyed that “the data is on the cluster” gets treated as the end of the job rather than the start of it, read on. /p h3Job description /h3 ullibBridge ingestion and use. /b 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. /li libMake data traceable. /b 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. /li libTune for the workload. /b Optimise parallel filesystems (Lustre, GPFS) and object storage for the concurrency, small‑file, and large‑checkpoint patterns of distributed GPU training and HPC simulation. /li libOperate at scale, safely. /b Design and run multi‑petabyte storage with the integrity and availability scientific work depends on – erasure coding, redundancy, hot‑to‑archival tiering. /li libAutomate everything. /b Deploy and scale storage and data services as code. Snowflake infrastructure doesn't survive at this scale. /li libMake it observable. /b Instrument storage health, capacity trends, and pipeline performance so problems surface before users feel them. /li libTranslate. /b Turn real access patterns from domain scientists and ML engineers into technical requirements – and push back when a request would quietly break something downstream. /li /ul pFor 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. /p pThe initial two‑year contract could potentially be extended or even become permanent. /p h3Profile /h3 ulliA technical degree (CS, engineering) or equivalent experience that demonstrates the same depth. /li liSolid storage grounding: filesystems (block and object), performance tuning, redundancy (RAID, erasure coding). /li liPython, and comfort automating infrastructure (Ansible, Terraform, or similar). /li liA working understanding of how ML and scientific workloads consume data – billions of small files, large checkpoints, sharding – and why naive layouts fall over. /li liA point of view on data lineage, provenance, or reproducibility – and ideally tooling you’ve used to enforce it. /li /ul h3What helps you stand out /h3 ulliHands‑on parallel filesystems (Lustre, Spectrum Scale/GPFS) or distributed storage (Ceph, VAST). /li liScientific data formats – HDF5, Zarr, Parquet – and opinions on when each earns its place. /li liObject storage (S3) interfaced with ML frameworks (PyTorch, TensorFlow). /li liOrchestration (Kubernetes, Argo) and data‑movement tooling. /li liData versioning / cataloguing (e.g. DVC, lakeFS, a metadata catalog) and familiarity with FAIR data principles. /li liCI/CD and provisioning: GitLab CI, HashiCorp Vault, MAAS. /li /ul pWe 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. /p h3What you get /h3 ulliHardware and scale you won’t find in enterprise IT – and problems with no vendor playbook. /li liWork that directly enables published science and frontier‑scale model training. /li liRoom to shape how data is managed, not just maintained, in an environment that takes it seriously. /li /ul h3Our core values as guiding principles /h3 ulliCuriosity: You enjoy learning and understanding systems deeply /li liOpenness: You collaborate effectively and value different perspectives /li liCourage: You are willing to tackle difficult or unfamiliar problems /li liSupportive: You help colleagues and users succeed /li liIntegrity: You act responsibly, reliably, and transparently /li /ul h3We offer /h3 ulliYour job with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society /li liYou can expect numerous benefits, such as public transport season tickets and car sharing, a wide range of sports offered by ASVZ, childcare and attractive pension benefits /li liYou can look forward to an exciting working environment, cultural diversity and attractive offers and benefits. /li liWe value the diversity of our team and, to further enhance the diversity of our workforce, we particularly encourage women to apply. /li /ul /p #J-18808-Ljbffr