Research Engineer

The healthcare systems of the future must harness data effectively to support clinicians, allowing them to focus on patient care while leveraging AIto detect patterns beyond human perception, enhance diagnostic accuracy, optimise workflows, improve risk assessment and communication. Developing AI models that address these needs is particularly urgent in ageing societies, where rising patient numbers coincide with increasing workforce constraints.

To do so, we are developing the AI for Science Instrumentation Gym, which is designed to bridge this gap by placing data-driven hypothesis generation at the center of its mission. It introduces a critical intermediate step: the tokenization and cartography of scientific data. Through tokenization, complex data is transformed into coarse-grained, interpretable units. Through cartography, these units are organized into latent spaces that can be explored as structured landscapes. In this way, machine learning becomes a tool for mapping high-dimensional data into forms that scientists can navigate, interpret, and use to generate new hypotheses.

To build the AIS Instrumentation Gym, we are opening a set of Instrumentation Gym Lead positions. IGLs are data scientists and systems builders who design, implement, and scale the AIS Instrumentation Gym across its different levels (S/M/L). They form 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 way.

As a Research Engineer, you will be one of the Gym Leads for the ML platform for the Scientific Instrumentation, and you will be 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 let representation models, tokenizers, and downstream models be swapped and composed
• Baseline tokenization and cartography pipelines, built collaboratively 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 lifted into the L-Gym tier without fundamental redesign

• Master’s degree on a relevant subject (e.g. Artificial Intelligence, Data Science, etc.)
• Strong Python and a modern ML framework (PyTorch preferred)
• Experience building data pipelines and reproducible ML workflows
• Comfort with GPU-based compute and basic scientific data formats
• Track record of building modular, maintainable software
• Interest in working at the intersection of science, ML, and systems engineering

Desirable:

• 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

• Accredited with 5 Tripartite Standards by Tripartite Alliance for Fair & Progressive Employment Practices (TAFEP) Singapore.
• A diverse workplace with 32 nationalities, offering ample opportunities for mutual learning.
• Positive and inclusive working environment
• 25 days of annual leave for fixed-term contracts
• 1 day of Birthday Leave
• Annual dental benefits
• Committed to being a supportive employer as you prioritize your physical and mental wellness
• Comprehensive healthcare insurance coverage
• Flexible hybrid work arrangement (up to 2 days per week from home)
• Abundant networking opportunities across various disciplines

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.