System Engineer ML Platform Cloud- HPC Infrastructure; Data-Centric Focus

System Engineer for ML Platform on a Cloud-like HPC Infrastructure (Data-Centric Focus)

100%, Singapore, fixed-term

The Singapore-ETH Centre was established in 2010 by ETH Zurich - The Swiss Federal Institute of Technology and Singapore’s National Research Foundation (NRF), as part of the NRF’s CREATE campus. As ETH Zurich's only research centre outside of Switzerland, the centre has strengthened the research capacity of ETH Zurich to develop sustainable solutions to global challenges in Switzerland, Singapore and the surrounding regions.

Set in Asia, in a rapidly urbanising region, the Singapore-ETH Centre aims to provide practical solutions to some of the most pressing challenges on urban sustainability, resilience and health through its programmes:
Future Cities Lab Global (FCL Global) and Future Health Technologies (FHT).

The centre serves as an intellectual hub for research, bringing together principal investigators and researchers from diverse disciplines and backgrounds. To promote the exchange of ideas and expertise, our researchers actively collaborate with universities and research institutes and engage with industry and government agencies to translate knowledge to practical solutions to real-world problems.

The AIS Instrumentation Gym is a multi-tier ML platform being built by both the National University of Singapore and the Singapore-ETH Centre (SEC) for shared scientific instruments (e.g., electron microscopy, X-ray imaging, and light microscopy). It supports researchers across three compute scales: laptop-based prototyping (S-Gym), single-GPU workstations (M-Gym), and multi-node HPC at the National Supercomputing Centre Singapore (L-Gym).

The Gym's purpose is to close the gap between high-dimensional scientific data and hypothesis generation. It does this by turning raw imaging data into learned, interpretable representations that scientists can explore as navigable landscapes: making patterns, anomalies, and structure legible at scales human intuition can no longer reach unaided.

As an ecosystem, the Gym brings together domain scientists, ML researchers, and systems builders around a common platform. We are now hiring full-time engineers and researchers to build, validate, and scale it.

• Manage the ML platform with a data-centric focus, ensuring infrastructure supports the full research lifecycle—from data tokenization to model cartography and validation—rather than just code deployment.
• Support data-centric CI/CD pipelines that ensure the automatic logging of artifact provenance (linking specific training data IDs, preprocessing logs, and model hyperparameters to deployment artifacts).
• Use automation frameworks (Ansible) to manage modular vService integrations, allowing the seamless incorporation of Token-Cartographer components (e.g., tokenization and alignment modules) into research environments.
• Ensure platform observability makes data and latent space embeddings visible and queryable for researchers throughout the model's lifetime.
• Maintain the interface to the infrastructure provisioning APIs to support dynamic, multi-tenant vCluster creation and resource allocation.
• Support the end-to-end provisioning and management of the ML platform, including secret vaults, git runners, and centralized artifact repositories.
• Position requires quarterly travel to Switzerland to synchronise with the core Swiss team.

• Must-haves:
  • A bachelor’s degree or higher in computer engineering, computer science, a relevant technical field, or equivalent practical experience.
  • You should have a knowledge of:
    • Microservice architecture
    • Linux administration skills
    • Automation tools and framework, including CI/CD processes and ecosystem (e.g., Gitlab CI, Hashi Corp Vault)
    • Dev…