Join an exciting biotech start-up in Edinburgh that’s developing next-generation technology relating to RNA sequencing, bioinformatics, and diagnostic development. Backed by academic expertise and driven by a mission to advance precision medicine, this agile team is developing tools to transform how RNA is discovered and analysed. As the company scales, it’s looking for a Bioinformatic Software Engineer to lead the build-out of cloud infrastructure and analysis pipelines critical to its technology platform.
This is an opportunity to join a growing, cross-functional team working on meaningful challenges in biology and data science, where your ideas and engineering skills will have a direct impact on product development and scientific discovery.

BIOINFORMATIC SOFTWARE ENGINEER REQUIREMENTS:

• Proven software engineering and DevOps experience within a research or R&D setting.
• Strong understanding of sequencing data analysis, particularly read alignment and variant calling algorithms.
• Degree educated in Computer Science, Bioinformatics, or a related field.
• At least 3 years’ relevant experience, ideally with RNAseq data and associated tool development.
• Solid programming skills in object-oriented languages and scripting languages (e.g. Python, Perl, Bash).
• Experience with software quality assurance practices such as version control, testing, and validation.

DESIRABLE EXPERIENCE:

• Commercial experience in a software or biotech setting.
• Cloud computing experience (e.g. AWS, GCP, or Azure).
• Familiarity with Unix/Linux systems.
• Knowledge of transcriptomic technologies such as Illumina, PacBio, or Nanopore.
• Understanding of transcriptome annotation and the impact of alternative splicing.
• Skills in R, C++, or similar for statistical analysis and visualisation.

• Design, develop, optimise, and maintain cloud computing environments for bioinformatic data processing.
• Build scalable, well-documented data analysis pipelines for long-read RNA sequencing workflows.
• Develop and implement logging, reporting, and data archiving systems to support reproducible research.
• Lead software engineering best practices, including testing, version control, deployment, and documentation.
• Generate visualisations and reports to communicate key findings from complex transcriptomic datasets.
• Collaborate closely with biologists, data scientists, and product stakeholders across the business.