The recent interdisciplinary collaboration between SNAC, the University of Sydney and I-MED will deliver a platform for seamless AI integration into clinical radiology workflows; and develop neuroimaging algorithms that will set a benchmark in diagnostic imaging, drive industry automation and productivity and mine hitherto untapped quantitative imaging data to improve health outcomes.  The collaboration is centred on industry-led innovation within the government science and research priority of Health and aligns with the Medical Technologies and Pharmaceuticals growth sector.

SNAC was established at the Brain Mind Centre to take advantage of unique research capacity in the field and provide a state-of-the-art commercial facility for the quantitative analysis of MRI to the pharmaceutical and other industries since 2012.  Together with a dedicated MS clinic, MS Clinical Trials Centre, and MRI unit, SNAC is co-located within the Brain & Mind Centre at the University of Sydney. SNAC offers a unique combination of in-house neuroscience, imaging science, clinical trial, neurology and radiology expertise; and specialises in building novel neuroimaging biomarkers and integrating these into Phase 2-4 clinical trials for the pharmaceutical industry.

Collaborative partners include:

The University of Sydney.

The University of Sydney is Australia’s premier University with an outstanding global reputation for academic and research excellence and employs over 7600 permanent staff supporting over 60,000 students. The University of Sydney's Brain and Mind Centre was established in 2015 for the research and treatment of critical health issues of the 21st century – disorders of the brain and mind. 

I-MED Radiology Network.

I-MED Radiology Network is the largest provider of clinical radiology service in Australia, providing over five million services a year to the Australian community across all states.


Key accountabilities and responsibilities

  • Provide specialized technical expertise in the design, planning and implementation of system solutions for medical imaging analysis applications in clinical settings.
  • Develop computing system architecture involving AI deep learning models.
  • Write maintainable and well-structured code and automated tests.
  • Review, troubleshoot, test and maintain the core code base to ensure strong optimization and functionality
  • Understand application security & identify problems and fixes.
  • Identify and implement scalable performance improvements.
  • Liaise with internal/external collaborators to deliver optimal outcomes.
  • Undertake any other duties relevant and appropriate to the position.
  • Maintain strict confidentiality of all project / commercial materials.

Knowledge, skills, experience and qualifications

  • Bachelor’s degree with 4+ years’ experience or a master’s degree in Computer Science or IT Engineering-related discipline with 2+ years’ professional experience.
  • Experience in two or more of Java, Python, C# and bash.
  • Experience in Web Sevices/Integration development using REST, JMS, etc. Experience with Python and AI framework is advantage.
  • Experience in building scalable architectures/data platforms within any public cloud (AWS, Azure or GCPs).
  • Knowledge of container tools such as Docker or singularity, and container orchestration tools such as Kubernetes, Docker Swarm, AWS ECS.
  • Professional experience of building CI/CD infrastructure and pipelines.
  • Solid understanding of development and solution design principles and non-functional requirements.
  • Knowledge of information security framework and incident management.
  • Good interpersonal and communication skills.
  • Analytical individual and self-motivated learner.
  • Knowledge of medical Imaging format (DICOM, etc.) and PACs Systems.
  • Experience with imaging software such as FSL, SPM, as well as Matlab will be desirable.


SNAC is an equal employment opportunity employer committed to equity, diversity and social inclusion. 

Please forward your application letter and CV to We will be reviewing applications as we receive them however due to the volume of the applications, please be informed that only shortlisted candidates will be contacted after the closing date.

Job Type: Full-time