Bioinformatic Software Engineer

York Place

9 months ago

Applications closed

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 responsibilities

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.

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.

Personal Attributes:

Curious and proactive, with a desire to learn and ask questions.

Strong communicator, able to collaborate across disciplines.

Thoughtful problem-solver with a strategic mindset.

Open, respectful, and team-oriented in working style.

This is a rare chance to join a well-supported start-up at an exciting stage of growth. You will be working on complex scientific problems with a direct line to product impact, in a collaborative environment where your contributions will shape the company’s direction and technology.

£Comp + company benefits

Bioinformatics/Software Engineering/RNA Seq/Python

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 11, 2026

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Jan 29, 2026

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.