Bayside Health
Bayside Health is a public health service delivering high-quality care across every stage of life for close to 1.2 million people living in metropolitan Melbourne, the Mornington Peninsula, Koo Wee Rup, Bass Coast and Southern Gippsland. We have more than 15 main sites, including hospitals, centres and clinics that provide comprehensive care from welcoming newborns to supporting older people and a full range of services in between. More than 22,000 dedicated staff are focused on providing exceptional, equitable, and locally connected care through shared expertise, compassion, and a commitment to continuous growth. Education and training are central to staff development as we encourage all employees to strive and thrive. Bayside Health was formed following the merger of Alfred Health, Bass Coast Health, Gippsland Southern Health Service, Kooweerup Regional Health Service and Peninsula Health on 1 January 2026.
- Fixed term Full Time (80 hrs/fn) + ADO) until Aug 2027
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Managers and Administrative Workers Grade 5
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Located at The Alfred
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Great staff benefits!
The Department
Alfred Health's Data Analytics Service (DAS) is transforming how data and AI shape patient care, operations, and research. We're looking for a Senior ML Engineer to build and operationalise the machine learning infrastructure that powers our clinical decision-support tools and analytical products, from classical ML through to LLMs and agentic AI.
Position Summary
This is a hands-on role where you'll design, build, and maintain the production-grade ML infrastructure that turns research prototypes into systems delivering measurable impact for patients and clinicians. Working closely with data scientists, clinicians, and technology teams, you'll bring deep technical expertise across the AI/ML stack, balanced with a strong commitment to quality, governance, and continuous improvement in a complex healthcare environment.
What you’ll do
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Design, build, and maintain scalable ML pipelines and infrastructure for training, evaluation, deployment, and monitoring
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Partner with data scientists and researchers to turn prototypes into robust, reproducible, production-ready systems
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Engineer and operationalise AI systems across modalities and paradigms; LLM-based and agentic applications, classical ML and other emerging architectures
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Lead model lifecycle management: versioning, A/B testing, drift monitoring etc.
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Ensure ML/AI systems comply with Bayside Health's AI governance, privacy, and security standards, including explainability and human-in-the-loop requirements
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Contribute to maturing data science standards and delivery frameworks
What we’re looking for
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Master's degree in a quantitative field
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5+ years in an ML or data engineering role
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Proven track record deploying production ML systems
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Cloud experience (Azure/Databricks) and healthcare highly regarded
Staff Benefits
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Generous salary packaging and novated leasing are available through Maxxia
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Onsite child care services, payroll deductible expense
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Onsite staff gym, a payroll deductible expense
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Modern onsite library faculties, exclusive to Alfred Health staff
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Access to health and wellbeing incentives
Join us in shaping the future of data-driven healthcare.
For enquiries regarding this role, please contact Theo Christian - Analytics Partnering Manager , on 0437 948 528
Applications close: 11pm AEST, Thursday 30th July 2026.
We embrace diversity and strive to have a workforce that reflects the communities we serve. We actively encourage applications from Aboriginal and Torres Strait Islander people, people with disability, and people of all genders, sexualities, and cultural backgrounds.
If you require adjustments to the recruitment and selection process, or require an alternative format to any of the application materials, please don’t hesitate to get in touch with the contact person listed on this ad.
In accordance with the Health Services Amendment (Mandatory Vaccination of Healthcare Workers) Act 2020, health care workers in Category A or B roles (as determined by the department’s risk ratings) are required to be vaccinated against influenza or hold an acceptable medical exemption.
