Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Bioinformatic Software Engineer

York Place
5 months ago
Applications closed

Related Jobs

View all jobs

Senior Research Engineer, Deep Learning for Cancer Genomics

Senior MLOps Engineer

Machine Learning Manager, London

Data Scientist

Data Scientist

Data Scientist

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.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

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

Jobs

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Jobs

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Jobs

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.