About The Company

I-MED Radiology Network is a leading medical imaging provider, supporting patients and clinicians across more than 200 clinics. With approximately 6 million patient procedures each year, our network brings together more than 350 doctors and 4,000 staff who share clinical knowledge and work toward excellent patient care. We are committed to using technology and data responsibly to support better clinical and operational outcomes.

Joining I-MED means becoming part of a collaborative healthcare organisation where innovation is connected to meaningful work. Our teams bring together expertise from clinical, technical and operational disciplines to improve services for patients and support the people who deliver their care.

About The Role

We are seeking a Junior Machine Learning Engineer to help develop and deliver scalable, reliable machine learning and artificial intelligence solutions across the organisation. This is a 12-month fixed-term, full-time opportunity based in Sydney or Melbourne.

As part of our Analytics team, you will work alongside Senior Machine Learning Engineers, Data Scientists, Software Engineers and clinical stakeholders. You will contribute throughout the solution lifecycle, helping move models and AI applications from development into production and supporting their ongoing operation. This role is suited to an engineer with a strong technical foundation who is keen to build practical experience delivering secure, dependable solutions in a healthcare environment.

Qualifications

You will bring two to three or more years of experience in machine learning engineering,



machine learning development or software engineering involving applied machine learning. A degree in Computer Science, Engineering, Data Science or a related discipline is required. Equivalent relevant experience may also be considered.

You should have strong Python programming skills and hands-on experience with SQL, Linux, Git and MLOps practices. You will understand machine learning fundamentals, including feature engineering and model evaluation, and have experience deploying machine learning models or AI solutions into production. Familiarity with CI/CD concepts and practices is important.

Experience with Docker, a cloud platform such as AWS, Google Cloud Platform or Azure, and batch or streaming data pipelines will be well regarded. Exposure to natural language processing or generative AI is also advantageous. Alongside your technical skills, you will bring a collaborative approach, explicit communication and an interest in developing solutions that meet clinical, security and governance requirements.

Responsibilities In this role, you will develop, train and evaluate machine learning models and contribute to building AI solutions. You will help deploy, integrate and operate scalable machine learning services and workflows,



ensuring they can be supported effectively in production.

You will develop and maintain machine learning pipelines for batch and real-time inference, and integrate solutions with enterprise platforms. You will also contribute to testing, monitoring and CI/CD practices that support reliable releases and ongoing model performance. Working closely with technical and clinical colleagues, you will help translate needs into practical solutions and support the delivery of high-quality, secure and appropriately governed systems.

Benefits

I-MED offers a range of benefits designed to support employee wellbeing and everyday life. These include discounted private health insurance, salary packaging, access to Fitness Passport, lifestyle and travel discounts, and access to I-MED services for eligible family members. Employee wellbeing support is also available.

This opportunity offers the chance to grow your engineering capability while contributing to machine learning and AI initiatives in a large medical imaging network. You will work with multidisciplinary colleagues and help shape solutions with practical relevance to healthcare delivery.

Equal Opportunity

I-MED Radiology Network is committed to a respectful and inclusive workplace. We welcome applications from people with diverse backgrounds and experiences and assess candidates on their skills, qualifications and suitability for the role. We encourage applicants who are interested in contributing to a collaborative team and supporting positive outcomes for patients and colleagues.

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