Vitalograph
Research Software Engineer – AI-Enabled Medical Devices
VitalographIreland6 days ago
Full-timeManufacturing
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.

Key Skills

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