Research Engineer / Research Engineeress

Research Engineer

100%, Singapore, Fixed-Term

The Singapore-ETH Centre, established in 2010 by ETH Zurich - The Swiss Federal Institute of Technology and Singapore's National Research Foundation (NRF), is ETH Zurich's only research center outside of Switzerland. It enhances ETH Zurich's research capacity to develop sustainable solutions to global challenges in Switzerland, Singapore, and the surrounding regions.

Located in Asia's rapidly urbanizing landscape, the Singapore-ETH Centre is dedicated to providing practical solutions to pressing challenges in urban sustainability, resilience, and health through its programs: Future Cities Lab Global (FCL Global) and Future Health Technologies (FHT).

The centre acts as an intellectual hub for research, uniting principal investigators and researchers from diverse disciplines and backgrounds. To encourage collaboration and the exchange of ideas, our researchers work closely with universities, research institutes, industry, and government agencies to translate knowledge into practical solutions for real-world challenges.

Project Background

The future of healthcare systems relies on effective data utilization to support clinicians, enabling them to concentrate on patient care while leveraging AI to detect patterns beyond human perception, enhance diagnostic accuracy, optimize workflows, and improve risk assessment. The urgency to develop AI models that meet these needs is particularly pronounced in aging societies, where increasing patient numbers coincide with workforce constraints.

To bridge this gap, we are developing the AI for Science Instrumentation Gym, which centers data-driven hypothesis generation as its mission. This initiative introduces a vital intermediate step: the tokenization and cartography of scientific data. Tokenization transforms complex data into coarse-grained, interpretable units, while cartography organizes these units into latent spaces, creating structured landscapes for exploration. This approach allows machine learning to be a tool for mapping high-dimensional data into forms that scientists can navigate, interpret, and use to generate new hypotheses.

To support the development of the AIS Instrumentation Gym, we are introducing a set of Instrumentation Gym Lead (IGL) positions. IGLs are data scientists and systems builders responsible for designing, implementing, and scaling the AIS Instrumentation Gym across its various levels (S/M/L). They serve as the infrastructure backbone of the ecosystem, enabling domain scientists and machine learning researchers to work with complex scientific data in a structured, scalable, and interpretable manner.

Job Description

As a Research Engineer, you will take on the role of one of the Gym Leads for the ML platform for Scientific Instrumentation, working on:

• Data pipelines - ingestion, metadata schemas, and provenance tracking for electron microscopy, X-ray, and light microscopy datasets
• Model interfaces - modular Python APIs that facilitate the swapping and composition of representation models, tokenizers, and downstream models
• Baseline tokenization and cartography pipelines, collaboratively built with ML researchers and domain scientists
• LLM- and RAG-assisted interfaces for dataset navigation, workflow discovery, and user interaction
• Forward compatibility to HPC - ensuring S-Gym and M-Gym components can be transitioned into the L-Gym tier without fundamental redesign

Profile

We are looking for candidates with the following qualifications:

• Master's degree in a relevant subject (e.g., Artificial Intelligence, Data Science, etc.)
• Strong proficiency in Python and modern ML frameworks (PyTorch preferred)
• Experience in building data pipelines and reproducible ML workflows
• Familiarity with GPU-based computation and basic scientific data formats
• Proven track record of developing modular, maintainable software
• Interest in the intersection of science, ML, and systems engineering

Desirable qualifications include:

• Experience with scientific imaging data
• Familiarity with LLMs and retrieval-augmented generation
• Background in interactive data tools (notebook UIs, dashboards, viewer apps)
• Experience deploying ML services on HPC or cloud GPU infrastructure

Workplace

We Offer

• Accredited with 5 Tripartite Standards by the Tripartite Alliance for Fair & Progressive Employment Practices (TAFEP) Singapore.
• A diverse workplace representing 32 nationalities, fostering mutual learning.
• A positive and inclusive working environment.
• 25 days of annual leave for fixed-term contracts.
• 1 day of Birthday Leave.
• Annual dental benefits.
• A commitment to support employee wellness—both physical and mental.
• Comprehensive healthcare insurance coverage.
• Flexible hybrid work arrangements (up to 2 days per week from home).
• Abundant networking opportunities across various disciplines.

We Value Diversity and Sustainability

ETH Zurich is dedicated to fostering an inclusive culture that promotes equality of opportunity and values diversity. We strive to create a working and learning environment where the rights and dignity of all staff and students are respected. Visit our Equal Opportunities and Diversity website to learn how we maintain a fair and open atmosphere for growth and development. Sustainability is a core value for us, and we continuously work toward a climate-neutral future.

Curious? So Are We.

We look forward to your online application using the form below.

• A cover letter detailing your motivation for the project
• Your CV, including the names and contact information of at least 2 references
• A copy of your university transcripts in PDF format

For further information about The Singapore-ETH Centre, please visit our website. Questions regarding the position should be directed to Prof. Duane Loh (NUS) at duaneloh@nus.edu.sg. (Strictly no applications).

Please note that only applications matching the job profile will be considered.