Software Research & Development Engineer

About

The Swiss Data Science Center (SDSC) is a national research infrastructure in data science and artificial intelligence (AI) of the ETH domain, with EPFL and ETH Zurich as founding partners. Its mission is to support academic labs, hospitals, industry and public sector stakeholders, including cantonal and federal administrations, through their entire data science journey, from the collection and management of data to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen), the SDSC provides expertise and services to various domains, such as health and biomedical sciences, energy and sustainability, climate and environment, and large‑scale scientific infrastructures.

Project Background

The SDSC is hiring a Software Research & Development Engineer to join its project‑based engineering team in Zürich. This team focuses on transforming research outcomes into production‑ready data science infrastructure. It operates in a complementary role to platform teams: exploring, building, and validating solutions before they are adopted as sustainable services.

You will work at the intersection of research and engineering, taking early‑stage ideas, prototypes, and emerging solutions, and turning them into reusable systems ready for real‑world deployment. This includes aligning with FAIR principles while ensuring that what is FAIR is also usable, scalable, and sustainable in practice.

Projects are driven by concrete needs across domains such as health and biomedical sciences, climate and environment, energy and sustainability, digital society, and large‑scale data ecosystems.

Job Description / Responsibilities

• Contribute to projects that evolve through two complementary modes:
  • Early phases: focused exploration and prototyping, shaping solution spaces, testing approaches, and making technical choices.
  • Mature phases: Minimum Viable Product (MVP) development, building operational, reusable components that can transition into production environments.
• Collaborate with engineers across the stack to build end‑to‑end solutions, primarily contributing to backend, data, and infrastructure components, while occasionally supporting lightweight user‑facing elements.
• Ensure continuity beyond the project lifecycle by working closely with internal platform teams and partner IT units to transition successful MVPs into production, making them maintainable, transferable, and ready for operational use.
• Co‑design solutions with users and domain experts, participate in collaborative workshops, and iteratively refine requirements into robust implementations.
• Follow established engineering and data best practices with a strong focus on reproducibility, maintainability, interoperability, and production readiness.

Profile / Requirements

• Open to candidates across different experience levels; problem‑solving approach and collaboration are key.
• Enjoy building systems that work in practice, comfortable navigating ambiguity, engaging with stakeholders, and iterating towards solutions.
• Care about quality, clarity, long‑term usability, and building secure‑by‑design systems aligned with best practices.
• Background in software engineering, data engineering, or a related field; interest in data‑intensive systems.
• Solid foundation in software or data engineering, typically through a Master’s degree or higher (e.g., PhD) in Computer Science or a related field, or equivalent professional experience. Experience in one of the application domains is a plus.
• Comfortable working at the interface between teams, bridging research, engineering, and operations to ensure sustainable adoption.
• Experience with modern software and data engineering practices such as version control, testing, APIs, data pipelines, containerisation, reproducible workflows (e.g., Docker, CI/CD, Nix), and programming in languages such as Python, Go, Rust, or similar.
• Exposure to data modelling or semantic interoperability (e.g., ontologies, common data models) is a plus.
• Attitude, curiosity, and a drive to learn are valued; technical skills can be developed on the job.

Workplace

• Location: Zurich, Switzerland
• Flexible work arrangements

We Offer / Benefits

• A stimulating, collaborative, cross‑disciplinary environment in a world‑class research institution
• Flexible work arrangements
• Exciting challenges, varied projects, and plenty of room to learn and grow
• Opportunity to follow your passion and use your skills to make an impact on research communities and society
• Possibility to spark your creativity by experimenting and learning new technologies
• Commitment to diversity, equality of opportunity, and an inclusive culture
• Strong focus on sustainability and a climate‑neutral future