Senior Machine Learning Engineer

🔥 13 hours ago

🇦🇺 Australia – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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DeepHealth

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

💰 $225k Grant on 2019-08

Healthcare • Consulting • Logistics

DeepHealth is a global leader in AI-powered health informatics, providing innovative solutions designed to enhance operational efficiency and clinical confidence in radiology. As a wholly-owned subsidiary of RadNet, Inc. , DeepHealth offers a comprehensive suite of diagnostic and imaging tools, including the DeepHealth OS, a pioneering cloud-native operating system. Their AI-powered solutions support large-scale diagnostic programs and streamline workflows in areas such as breast, prostate, lung, and brain cancer detection. DeepHealth collaborates with leading healthcare institutions worldwide to transform radiology practices and improve care delivery.

📋 Description

• Improve existing production models through systematic error analysis, better data, targeted experiments, and changes to model architecture and training • Develop models for new products from initial formulation and feasibility experiments through training, validation, and production integration • Partner with clinicians and product colleagues to define meaningful evaluation criteria, including sensitivity, specificity, and clinical consequences of different error types • Evaluate robustness across patient populations, clinical sites, imaging equipment, and acquisition conditions • Identify performance gaps and build evidence that improvements generalize • Improve data curation and annotation workflows, including coverage gaps, label quality, and prevention of data leakage • Build reproducible training and evaluation pipelines with traceable datasets, experiments, and model versions • Partner with software engineers to optimize inference speed, resource use, and operational reliability • Investigate model issues that emerge in production • Review relevant research and test promising approaches • Contribute to validation and technical documentation with quality and regulatory colleagues • Review code and experiments, mentor colleagues, and communicate findings and trade-offs clearly

🎯 Requirements

• Bachelor's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience (required) • 5+ years of hands-on experience developing and delivering machine learning models • Experience independently taking complex work from an initial problem to a working solution • Strong foundations in deep learning and computer vision, including practical experience with image classification, detection, or segmentation • Strong Python skills • Experience with a modern deep learning framework such as PyTorch • Track record of deploying models into products and measuring performance beyond development datasets • Rigor in experimental design and evaluation, including appropriate baselines, uncertainty, failure-mode analysis, and distinguishing meaningful gains from noise • Strong software engineering practices, including maintainable code, testing, version control, and reproducibility • Sound judgment regarding trade-offs between model quality, complexity, inference cost, and delivery time • Ability to work autonomously and collaborate effectively across disciplines, with clear written communication • Follows data privacy, compliance, safety, confidentiality, employment laws, and DeepHealth policies and procedures • Preferred: Medical imaging experience or other applications involving variable image quality and limited or noisy labels • Preferred: Developing and validating models for regulated products • Preferred: Self-supervised learning, transfer learning, or foundation models for computer vision • Preferred: Distributed training, cloud infrastructure, or inference optimization • Preferred: Monitoring deployed models and addressing changes in data or performance over time

🏖️ Benefits

• Remote work arrangement • Occasional travel may be required