Machine Learning Engineer

Machine Learning Engineer (Medical Imaging AI)

KEY POINTS

• Next-generation cardiovascular imaging products

• $120,000 + Bonus + Equity

• High-impact role applying cutting-edge deep learning to real-world clinical problems (CT & MR imaging)

ABOUT THE CLIENT

We’re supporting a globally recognised medical imaging technology company at the forefront of cardiovascular diagnostics, building intelligent software used by leading healthcare institutions worldwide.

Their platform sits at the intersection of advanced imaging, clinical research, and machine learning combining large-scale medical datasets with state-of-the-art AI to improve how cardiovascular disease is diagnosed and understood.

Following continued success and product expansion, they are investing heavily in their AI capability, with a particular focus on building robust, production-grade machine learning systems that directly impact patient outcomes.

This is not a research lab environment detached from reality this is applied, high-stakes AI, where models are deployed into real clinical workflows and used by physicians globally.

THE MACHINE LEARNING ENGINEER ROLE

This is a highly technical, hands-on role focused on designing and building the core machine learning systems that power advanced cardiovascular imaging products.

You will take ownership of key components across the ML lifecycle from data pipelines and model design through to deployment in production environments.

The work is deeply challenging, sitting at the intersection of computer vision, 3D data, and biomedical science translating complex academic ideas into scalable, real-world systems.

You will work closely with a small, highly capable team of engineers and scientists, contributing to both the research direction and the productionisation of models.

Key responsibilities include:

• Designing and deploying deep learning models for 3D medical image segmentation and analysis

• Working with complex CT and MR imaging datasets to extract clinically meaningful insights

• Translating cutting-edge research into robust, production-ready ML systems

• Building scalable pipelines from data ingestion through to model deployment

• Writing clean, well-tested, high-performance Python code

• Collaborating with cross-functional teams across engineering, product, and research

• Contributing to technical direction and model architecture decisions

• Staying close to the latest developments in medical imaging AI (e.g. MICCAI) and applying them where relevant

ESSENTIAL SKILLS

• PhD in Computer Science, Machine Learning, AI, or a closely related field

• 0–3 years’ industry experience (or postdoctoral research) applying machine learning to real-world problems

• Strong grounding in deep learning, particularly computer vision or 3D data

• Experience working with complex datasets (ideally medical or scientific imaging)

• Proficiency in Python and at least one major ML framework (PyTorch or TensorFlow)

• Solid understanding of software engineering fundamentals (version control, testing, clean code)

• Evidence of translating research into practical implementations

NICE TO HAVE

• Experience working with medical imaging data (e.g. DICOM)

• Exposure to 3D image segmentation or geometric modelling

• Familiarity with MLOps tooling (Docker, Kubernetes, MLflow)

• Experience deploying ML models in cloud environments (AWS, GCP, Azure)

• Publications in top-tier conferences (e.g. MICCAI, NeurIPS, CVPR)

TO BE CONSIDERED

Please either apply by clicking online or emailing me directly at [email protected]. I can make myself available outside of normal working hours between 7am and 8pm. If unavailable, please leave a message and either myself or one of my colleagues will respond.

By applying for this role, you give express consent for us to process and submit (subject to required skills) your application to our client in conjunction with this vacancy only.

KEY SKILLS

Machine Learning Engineer, Deep Learning, Medical Imaging, Computer Vision, 3D Image Segmentation, PyTorch, TensorFlow, Python, DICOM, MLOps, Healthcare AI, Research to Production