Job Description
Location: Ennis, Ireland or Buckingham, UK
Why Join Us?
- Generous pension scheme with company contributions
- Company contributed healthcare scheme
- Death in service benefit (4x annual salary)
- A collaborative, friendly work culture
Role Overview
We 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 Responsibilities
- Refactor, 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 Duties
- Quality 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 & Qualifications
- Bachelor’s or Master’s degree in Software Engineering, Computer Science, Computational Science, Data Science, or a related field.
Required
Key Skills & Experience
- 3+ 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 Success
- Timely 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.