Research Software Engineer – AI-Enabled Medical Devices
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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.
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.
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