Software Engineer
DeepHealth
Software Engineer
DeepHealth
AI-driven medical imaging and informatics platform
Job Summary
In the Clinical AI Devices team, the Software Engineer is primarily responsible for building and maintaining software in the Clinical AI domain, from wrapping deep learning models into containerized medical devices, to integration with other systems. This work spans all of the company's medical devices, across the main radiology modalities: X-Ray, CT, MRI, and Mammography. The focus of the current role is at the interface between product logic and the underlying serving platform, ensuring that requests are correctly validated, routed, and that results are properly assembled and returned.
Essential Duties and Responsibilities
Participate in the software development process: design, refine, and develop new features, and maintain existing ones.
Write code, review teammates' code, and contribute to the testing framework through automated and manual tests. Work with the team lead and product manager to refine requirements.
Own features and projects end-to-end, from refinement to production. Flag risks and bugs by keeping the radiologist's workflow in mind. Support service and delivery teams with production issues.
Ensure compliance with Quality and Regulatory requirements throughout the development cycle, including technical documentation (e.g. implementation specs), cybersecurity processes, and risk analysis.
Collaborate with the team on strategic recommendations and improvements to technology and business.
PLEASE NOTE: This is not an exhaustive list of all duties, responsibilities and requirements of the position described above. Other functions may be assigned, and management retains the right to add or change duties at any time.
AI-driven medical imaging and informatics platform
DeepHealth provides AI-powered medical imaging and health informatics solutions that help radiologists analyze X-rays, CTs, and MRIs by automating parts of image interpretation, highlighting areas of concern, and delivering quantitative insights. It works by integrating AI into radiology workflows via cloud-based software, enabling automated image analysis, scoring, and reporting within enterprise imaging systems. The company differentiates itself through its integration with RadNet and strategic acquisitions of iCAD and CIMAR UK, expanding breast imaging capabilities, interoperability, and the scale of its deployments. Its goal is to accelerate the adoption of AI in real-world healthcare, improve diagnostic accuracy and consistency, and streamline imaging workflows across hospitals and health systems.
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