Software Research & Development Engineer

Software Research & Development Engineer

EPFL - Swiss Federal Institute of Technology Lausanne

Lausanne, Switzerland

About the role

You will work on one of the SDSC’s innovation partnerships in the French part of Switzerland. In this role, you will meet partners (companies) to understand their needs and help them define a high-impact project with the SDSC. You will be responsible for successfully carrying out the project thanks to your machine learning expertise with the help and support of the SDSC team.

The Swiss Data Science Center (SDSC) is hiring a Software Research & Development Engineer to join its project-based engineering team in Geneva, Lausanne, or 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 going further: 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.

Your tasks

You will contribute to projects that evolve through two complementary modes. In early phases, you will engage in focused exploration and prototyping, shaping solution spaces, testing approaches, and making technical choices. As projects mature, you will contribute to Minimum Viable Product (MVP) development, building operational, reusable components that can transition into production environments. You will collaborate with engineers across the stack to build end-to-end solutions, contributing primarily to backend, data, and infrastructure components, while occasionally supporting lightweight user-facing elements where needed.

A key part of the role is to ensure continuity beyond the project lifecycle. You will work closely with internal platform teams and partner IT units to transition successful MVPs into production, ensuring they are maintainable, transferable, and ready for operational use. Across all phases, you will co-design solutions with users and domain experts, participate in collaborative workshops, and iteratively refine requirements into robust implementations.

Your profile

We are open to candidates across different levels of experience. You may be early in your career or already experienced; what matters most is your approach to problem-solving and collaboration.

You enjoy building systems that work in practice, not just in theory. You are comfortable navigating ambiguity, engaging with stakeholders, and iterating towards solutions. You care about quality, clarity, long-term usability, and building systems that are secure by design and aligned with best practices.

You likely have a background in software engineering, data engineering, or a related field, and an interest in data-intensive systems. You bring a solid foundation in software or data engineering, typically developed 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, but not required.

Importantly, you are comfortable working at the interface between teams, helping bridge research, engineering, and operations, and ensuring that what is built can be successfully adopted and sustained.

You may have 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. We do not expect you to know every technology we use. We value attitude, curiosity, and a drive to learn, technical skills can be developed on the job.