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The Swiss National Supercomputing Centre (CSCS), operated by ETH Zurich, develops and operates a high-performance computing and data research infrastructure supporting world-class science in Switzerland. The centre provides its user laboratory to researchers in academia, industry, and the business sector, with offices in Lugano and Zurich. For this position, the work location is either Lugano or Zürich, with a two-year contract. The role focuses on bridging the gap between raw data storage and usable, traceable, reproducible data, supporting projects such as those in the weather and climate domain aimed at understanding and mitigating the impact of climate change.

Tasks

  • Design pipelines and metadata that turn ingested data into findable and consumable formats, including catalogs, schemas, and access layers that match how training jobs and simulations read data.
  • Build lineage and provenance systems so any dataset, checkpoint, or result can be traced back to its inputs and transformations, ensuring reproducibility as a first-class requirement.
  • Optimise parallel filesystems (Lustre, GPFS) and object storage for the concurrency, small-file, and large-checkpoint patterns of distributed GPU training and HPC simulation.
  • Design and run multi-petabyte storage with integrity and availability, including erasure coding, redundancy, and hot-to-archival tiering.
  • Deploy and scale storage and data services as code, automating infrastructure to handle large-scale environments.
  • Instrument storage health, capacity trends, and pipeline performance to surface problems before users experience them.
  • Translate real access patterns from domain scientists and ML engineers into technical requirements and provide feedback on requests that may impact downstream systems.

Requirements

  • A technical degree (CS, engineering) or equivalent experience demonstrating similar depth.
  • Solid grounding in storage: filesystems (block and object), performance tuning, redundancy (RAID, erasure coding).
  • Experience with Python and comfort automating infrastructure (Ansible, Terraform, or similar).
  • Understanding of how ML and scientific workloads consume data, including billions of small files, large checkpoints, and sharding, and awareness of the limitations of naive layouts.
  • Perspective on data lineage, provenance, or reproducibility, and ideally experience with related tooling.
  • Hands-on experience with parallel filesystems (Lustre, Spectrum Scale/GPFS) or distributed storage (Ceph, VAST) is a plus.
  • Familiarity with scientific data formats (HDF5, Zarr, Parquet) and their appropriate use cases.
  • Experience with object storage (S3) interfaced with ML frameworks (PyTorch, TensorFlow).
  • Knowledge of orchestration (Kubernetes, Argo) and data-movement tooling.
  • Experience with data versioning/cataloguing (e.g. DVC, lakeFS, metadata catalog) and FAIR data principles.
  • Familiarity with CI/CD and provisioning tools such as GitLab CI, HashiCorp Vault, MAAS.
  • Depth in storage or data engineering and curiosity to grow into the other area is valued more than a complete checklist.

Benefits

  • Access to hardware and scale not found in enterprise IT, and the opportunity to solve unique problems.
  • Work that directly enables published science and frontier-scale model training.
  • Room to shape how data is managed in an environment that values data management.
  • Support for professional development and contribution to positive societal change as part of ETH Zurich.
  • Benefits such as public transport season tickets, car sharing, a wide range of sports offered by ASVZ, childcare, and attractive pension benefits.
  • An exciting working environment with cultural diversity and attractive offers.
  • Commitment to building a diverse and inclusive engineering team, with encouragement for applications from underrepresented groups in tech, especially women.
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Über uns
Die ETH Zürich ist eine technisch-naturwissenschaftliche Hochschule des Bundes mit Sitz in Zürich. Wir betreiben Lehre, Forschung und Wissenstransfer in Bereichen wie Ingenieurwissenschaften, Naturwissenschaften, Architektur, Mathematik, Informatik, Management sowie Sozial- und Geisteswissenschaften. Unsere Ausbildung umfasst Bachelor-, Master-, Doktorats- und Weiterbildungsangebote und ist eng mit internationaler Spitzenforschung verbunden. Als Teil des ETH-Bereichs entwickeln wir wissenschaftliche Grundlagen, Technologien und Lösungen für Gesellschaft, Wirtschaft und globale Herausforderungen.
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