Job Description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in Australia.
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Join an applied machine learning environment focused on developing AI solutions that deliver meaningful improvements in clinical products.
n You'll work across the full ML lifecycle, from data and experimentation through model development, evaluation, and production deployment.
n The role combines deep learning and computer vision expertise with strong software engineering and experimental rigor.
n You'll improve production models while also developing new solutions from initial problem formulation through validation and integration.
n Working closely with machine learning engineers, software engineers, clinicians, and product teams, you'll translate complex problems into measurable technical outcomes.
n A major focus will be ensuring models are robust across diverse patient populations, clinical environments, imaging equipment, and acquisition conditions.
n This is a hands-on opportunity to contribute to high-impact AI products while shaping reliable, reproducible, and production-ready machine learning systems.
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Accountabilities:
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Improve existing production machine learning models through systematic error analysis, improved data, targeted experimentation, and changes to model architectures and training approaches.
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Develop machine learning models for new products, taking problems from initial formulation and feasibility experiments through training, validation, and production integration.
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Collaborate with clinicians and product stakeholders to define meaningful evaluation criteria, including sensitivity, specificity, and the clinical implications of different error types.
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Evaluate model robustness across patient populations, clinical sites, imaging equipment, and acquisition conditions, identifying performance gaps and generating evidence that improvements generalize effectively.
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Improve data curation and annotation workflows by addressing coverage gaps, label quality, and potential sources of data leakage.
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Build reproducible training and evaluation pipelines with traceable datasets, experiments,



and model versions.
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Partner with software engineers to optimize inference performance, resource consumption, and operational reliability, while investigating model issues that arise in production.
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Research relevant scientific developments, test promising approaches, and make evidence-based decisions about technologies and methodologies to adopt.
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Contribute to model validation and technical documentation in collaboration with quality and regulatory teams.
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Review code and experiments, provide constructive technical feedback, mentor colleagues, and communicate technical findings, risks, and trade-offs clearly.
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Follow applicable data privacy, compliance, safety, confidentiality, quality, and regulatory standards throughout the development and delivery lifecycle.
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Maintain a professional, collaborative, and accountable approach while adapting to new technologies, methods, systems, and responsibilities.
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Requirements:
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Bachelor's degree in Computer Science, Engineering, Mathematics, or a related discipline, or equivalent practical experience.
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5+ years of hands-on experience developing and delivering machine learning models, with demonstrated ability to independently take complex problems from initial formulation through to working solutions.
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Strong foundations in deep learning and computer vision, including practical experience with image classification, object detection, or image segmentation.
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Advanced Python skills and experience with a modern deep learning framework such as PyTorch.
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Proven experience deploying machine learning models into production products and measuring their performance beyond development datasets.
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Strong experimental design and evaluation skills, including appropriate baselines, uncertainty analysis, failure-mode analysis,



and the ability to distinguish meaningful improvements from statistical or experimental noise.
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Strong software engineering practices, including maintainable code, testing, version control, documentation, and reproducibility.
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Sound technical judgment when balancing model quality, complexity, inference costs, resource requirements, and delivery timelines.
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Ability to work autonomously while collaborating effectively with multidisciplinary teams, with strong written and verbal communication skills.
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Experience with medical imaging or other applications involving variable image quality and limited or noisy labels is highly desirable.
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Experience developing and validating models for regulated products is an advantage.
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Knowledge of self-supervised learning, transfer learning, or foundation models for computer vision is a plus.
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Experience with distributed training, cloud infrastructure, or inference optimization is beneficial.
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Experience monitoring deployed models and addressing changes in data distributions or model performance over time is desirable.
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Strong professionalism, integrity, confidentiality, adaptability, and commitment to quality and timely delivery.
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Benefits:
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Remote/hybrid working workplace.
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Position based in Melbourne, Victoria, Australia.
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Opportunity to work on applied machine learning and computer vision solutions with meaningful clinical applications.
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End-to-end exposure across data, experimentation, model development, validation, deployment, and production monitoring.
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Collaboration with machine learning engineers, software engineers, clinicians, product teams, and quality and regulatory specialists.
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Opportunity to work on challenging problems involving model robustness, clinical variability, computer vision, and production AI.
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Scope to influence technical decisions around model architecture, experimentation, inference optimization, and engineering practices.
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Opportunities to mentor colleagues and contribute to the evolution of machine learning development standards and workflows.
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The source posting does not specify a salary range or additional formal benefits.
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📌 Senior Machine Learning Engineer (Victoria)
🏢 Jobgether
📍 Victoria