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 ML Research Engineer / Scientist based in Australia.
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Join a research-driven team building next-generation AI models designed to understand complete CT studies rather than isolated findings.
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You'll work on foundation models, vision-language learning, and multi-finding detection using medical imaging data at unprecedented scale.
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Your research will have a direct path from experimentation to regulatory submissions, hospital deployment, and real-world patient care.
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You'll own models and experiments end to end, from developing the initial idea through training, evaluation, calibration, and production readiness.
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You'll work alongside experienced ML engineers, software engineers, and fellowship-trained radiologists across multiple clinical specialties.
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The role combines deep technical research with practical impact, giving you the opportunity to solve challenging problems in medical AI with exceptionally rich real-world data.
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This is a fully remote prospect for an independent researcher who wants their work to move quickly from the lab into clinical practice.
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Accountabilities
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Design, develop, train, and evaluate machine learning models capable of interpreting complete CT studies at the study level.
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Research foundation-model approaches for medical imaging, including 3D and volumetric learning at large scale.
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Develop and investigate vision-language models that connect medical images with the terminology and reporting patterns used by radiologists.
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Build models capable of identifying and prioritizing multiple urgent clinical findings simultaneously while maintaining safe and clinically appropriate operating points.
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Design and execute independent experiments, from hypothesis formation and architecture selection through training, evaluation, and analysis.
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Develop custom architectures, training pipelines, loss functions, and distributed training approaches using modern deep learning frameworks.
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Analyze model performance rigorously and establish reproducible evaluation methodologies suitable for clinically consequential AI systems.
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Work closely with fellowship-trained radiologists to understand clinical requirements, interpret results, and translate research findings into practical model improvements.
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Contribute to models and research that progress toward regulatory submissions, clinical deployment, and real-world patient use.
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Take ownership of research projects end to end and make informed decisions about which experiments and approaches are most likely to deliver meaningful improvements.
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Collaborate with ML and software engineering teams to move successful research from experimentation toward robust, deployable systems.
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Requirements:
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Strong practical experience with contemporary machine learning and deep learning, particularly using PyTorch for custom architectures, training loops, and experimentation.
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Deep understanding of why machine learning architectures, objectives, optimization strategies, and training approaches work, rather than relying solely on existing implementations.
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Demonstrated ability to independently formulate hypotheses, design experiments, interpret results, and iterate toward better models.
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Strong understanding of rigorous experimentation, evaluation, reproducibility, and model validation.
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Experience working with large-scale datasets and distributed training environments is highly valuable.
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A strong interest in solving technically challenging problems where model performance and reliability have meaningful real-world consequences.
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Ability to work effectively with researchers, engineers, and clinical experts in a collaborative environment.




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Medical imaging, 3D computer vision, or volumetric-data experience is advantageous but not required.
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Experience with vision-language models or self-supervised learning is a plus.
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Familiarity with DICOM, CT imaging, radiology, or other medical-data formats and workflows is beneficial.
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A PhD, research publications, or a strong academic research background is a plus, but not a prerequisite.
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Prior medical-AI experience is not required; a willingness to learn clinical concepts directly from radiology experts is valued.
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Strong written and verbal communication skills and the ability to work independently in a fully remote environment.
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Benefits:
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Fully remote position open to candidates worldwide.
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Location-flexible compensation with a cash-weighted base salary determined according to the local market in the country where you work.
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Specific compensation range for your location shared early in the hiring process.
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No equity included in international offers, with compensation structured transparently around local-market cash pay.
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Opportunity to work with a real-world CT dataset covering approximately 10 million patients, paired with radiology reports.
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Direct collaboration with fellowship-trained radiologists across areas including chest, body, MSK, neuro, and oncology.
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Opportunity to work on research that can progress from experimentation to FDA submissions, hospital deployments, and patient care within months.
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Exposure to large-scale foundation models, vision-language learning, distributed training, medical imaging, and clinically focused AI evaluation.
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High degree of ownership over research ideas, experiments, models, and technical direction.
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Opportunity to work alongside researchers and engineers with significant contributions to medical AI, open datasets, algorithms, and clinical AI systems.
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A small, research-oriented team where successful ideas can move quickly from research into production.
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📌 Ml Research Engineer / Scientist (New South Wales)
🏢 Jobgether
📍 New South Wales