Research Software Engineer – AI-Enabled Medical Devices

Job DescriptionLocation: Ennis, Ireland or Buckingham, UKWhy Join Us?Generous pension scheme with company contributionsCompany contributed healthcare schemeDeath in service benefit (4x annual salary)A collaborative, friendly work cultureRole OverviewWe are seeking a skilled Research Software Engineer to support the transformation of cutting-edge research algorithms into robust, production-grade software modules for deployment in regulated medical devices. This role requires a strong foundation in software engineering principles, a commitment to quality and documentation, and the ability to work within a structured software development lifecycle (SDLC).The successful candidate will play a key role in preparing signal processing, AI, and machine learning (ML) algorithms for regulatory approval (e.g., FDA, MDR), working closely with cross-functional teams including Clinical Data Science, Software R&D, Data Management, and Regulatory Affairs. There may also be opportunities to contribute to academic or collaborative research initiatives.Primary ResponsibilitiesRefactor, test, and maintain research algorithm codebases, primarily in Python and MATLAB, with additional support for languages such as C# where required.Translate research prototype code into production-grade software modules and deployable executables.Develop and document automated pipelines for data processing, algorithm validation, and reproducibility.Package and prepare algorithms for demonstration, analysis, and regulatory submission.Ensure all work adheres to structured SDLC processes, including the creation of technical documentation such as User Requirements Specifications (URS) and Software Architecture Documents (SAD).Support the Clinical Data Science team by implementing research outcomes in a reproducible, efficient, and production-ready manner.Assist with integration into graphical user interfaces (GUIs) or lightweight visualisation tools when needed.Provide general technical support to the Clinical Data Science team as required.Core DutiesQuality assurance, code refactoring, modernisation, and deployment of signal processing/AI/ML research algorithms.Development and maintenance of tools and pipelines for algorithm testing, validation, and deployment.Creation of comprehensive and traceable technical documentation as part of a regulated development environment.Collaborate cross-functionally to execute, present, and deliver high-quality software components.Education & QualificationsBachelor’s or Master’s degree in Software Engineering, Computer Science, Computational Science, Data Science, or a related field.RequiredKey Skills & Experience3+ years of experience in software development within scientific or research environments.Proficiency in Python and MATLAB, including object-oriented programming, testing frameworks, and packaging best practices.Experience working within a structured SDLC, with a strong understanding of technical documentation requirements (e.g., URS, SAD).Proficiency with version control systems (e.g., Git) and CI/CD workflows.Preferred:Experience deploying ML models in cloud-based, commercial, or regulated environments.Proven ability to bridge research prototypes and production systems.Direct experience contributing to software intended for regulatory submission(e.g., FDA, MDR), including packaging, traceability, and technical documentation for audits or validations.Familiarity with signal processing and ML/AI tools and libraries such as scikit-learn, TensorFlow, PyTorch, and MATLAB toolboxes.High attention to detail, with a strong commitment to code quality and documentation.Practical problem-solving skills with a systems-oriented mindset.Clear and effective communication with both technical and non-technical stakeholders.Initiative in improving tooling, processes, or codebase organisation.Strong written and verbal communication skills.Ability to work both independently and collaboratively.Strong interpersonal skills with the ability to engage effectively with internal and external stakeholders at all levels.Key Measures of SuccessTimely and accurate delivery of production-ready code and documentation derived from research prototypes.Consistent contributions to software quality, maintainability, and automated testing frameworks.Establishment of sustainable engineering practices that support the transition of algorithms to validated commercial-grade products.Effective cross-functional collaboration with teams such as Clinical Data Science, Software R&D, Data Management, and Regulatory Affairs.Positive feedback from stakeholders on the clarity, usability, and robustness of delivered software components.