Introduction
A Research Software Engineer (RSE) plays a crucial role in bridging the gap between research and software development. The tasks and responsibilities in this role can vary significantly depending on the research domain, but they generally focus on ensuring that software they develop to support research is robust, sustainable, reliable, reusable, and efficient.
RSEs have a deeper understanding of engineering practices, performance, software quality and tooling and work alongside researchers to translate scientific problems into robust software solutions and are often required to work across multiple projects or domains.
Software development & quality responsibilities, challenges & tasks
Main responsibilities and common challenges of this role in relation to research software development and Research Software Quality include:
- Collaboration with researchers: translating research problems into technical requirements and help implement solutions
- Software development: writing and maintaining code for research projects (prototyping, optimisation, refactoring)
- Code quality and best practices: applying best practices like version control, testing, documentation, and continuous integration
- Reproducibility & sustainability: ensuring research outputs are reproducible and software is maintainable
- Data management: handling data wrangling, storage, format conversions, and pipeline automation
- Performance optimisation: improving performance of simulations, analyses, or computational workflows
- Tool integration: helping integrate existing tools and frameworks into research environments
- Training: educating researchers and early career RSEs on coding practices and tools
Some example domain-specific tasks of RSEs are listed below.
Life & health sciences:
- process and analyse genomic or proteomic data
- develop tools for sequence alignment, gene expression analysis, or CRISPR design
- optimise bioinformatics pipelines using tools like Snakemake or Nextflow
- build machine learning pipelines for medical imaging or patient data
- support integration with electronic health record systems
Physics & astronomy:
- implement or optimise simulations (e.g., quantum mechanics)
- parallelise code for HPC (using MPI/OpenMP)
- analyse big data (massive datasets) produced by modern telescopes
- visualise simulation results
Social sciences & digital humanities:
- build web applications for interactive data visualisations
- process and analyse textual or multimedia data using NLP techniques
- design databases for archival and metadata handling
Environmental sciences:
- develop and maintain climate models or Earth system models
- automate data assimilation pipelines
- visualise geospatial data using GIS tools
Software development & quality guidance
- Organising software projects - a well-structured project directory facilitates collaboration, maintenance, and reproducibility
- Adopting FAIR principles promotes transparency and facilitates wider adoption of research software
- Code quality best practices - version control, testing, documentation and licencing
- Software testing practices and automating tests using Continuous Integration (CI)
- Engaging in code reviews - regular code reviews contribute to code quality and facilitate knowledge sharing among team members
Editor
The University of Manchester / Software Sustainability Institute