About The Company

I-MED Radiology Network is a leading medical imaging provider, bringing together a community of professionals committed to excellence in patient care. With more than 200 clinics and approximately six million patient procedures each year, I-MED is one of the largest medical imaging providers in the world. Our network includes more than 350 doctors and 4,000 staff members who share clinical knowledge and expertise to support patients and communities.

At I-MED, innovation and collaboration help us deliver high-quality services and improve the way we work. Our teams combine clinical insight with technical capability to support better outcomes across the organisation. Join us and contribute to the responsible development of machine learning and AI solutions in a healthcare environment.

About The Role

We are seeking a Junior Machine Learning Engineer to help develop and deliver scalable, reliable machine learning and AI solutions that support clinical and operational outcomes. Based in Sydney or Melbourne, you will work within our Analytics team and collaborate with Senior Machine Learning Engineers, Data Scientists, Software Engineers and clinical stakeholders.

You will contribute across the solution lifecycle, helping take models and AI applications from development into production. This role is suited to an early-career engineer who has practical experience applying machine learning and is keen to build expertise in production systems, MLOps and the healthcare sector. You will work with experienced colleagues to deliver secure, well-governed solutions that can be integrated into enterprise platforms.





Qualifications To be considered, you will bring two to three or more years of experience in machine learning engineering, machine learning development, or software engineering with applied machine learning. You will have strong Python programming skills and hands-on experience with SQL, Linux, Git and MLOps practices.

You should understand machine learning fundamentals, feature engineering and model evaluation, and have experience deploying machine learning models or AI solutions in a production environment. Familiarity with CI/CD practices is expected. A degree in Computer Science, Engineering, Data Science or a related discipline is required.

Experience with Docker, cloud platforms such as AWS, Google Cloud Platform or Microsoft Azure, and batch or streaming data pipelines will be highly regarded. Knowledge of natural language processing or generative AI is also beneficial. We value clear communication, a collaborative approach, attention to quality and an interest in applying technology thoughtfully in a clinical setting.

Responsibilities In this role, you will develop, train and evaluate machine learning models and contribute to the creation of AI solutions. You will help deploy,



integrate and operate scalable machine learning and AI services, workflows and applications. You will build and maintain machine learning pipelines, including batch and real-time inference, and support integration with enterprise platforms.

You will implement and contribute to testing, monitoring and CI/CD practices to improve the reliability and maintainability of solutions. Working closely with technical and clinical teams, you will help translate requirements into practical outcomes. You will also contribute to secure, governed delivery, share progress with stakeholders and follow established engineering and organisational standards throughout development and operation.

Benefits

I-MED offers a range of perks designed to support your wellbeing and lifestyle. These include discounted private health insurance, salary packaging, access to Fitness Passport, lifestyle and travel discounts, and access to I-MED services for your family. Employee wellbeing support is also available. You will have the opportunity to work alongside experienced specialists and contribute to technology initiatives with meaningful clinical and operational applications.

Equal Opportunity

I-MED Radiology Network is committed to providing an inclusive and respectful workplace. We welcome applications from people with diverse backgrounds, experiences and perspectives, and assess candidates fairly against the requirements of the role. We encourage applicants to bring their skills and ideas to a collaborative setting focused on delivering high-quality care and services.

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