Machine Learning Engineer
DeepHealth, a fully owned subsidiary of RadNet, provides AI-powered health
informatics to empower breakthroughs in care delivery. The heart of our portfolio of
solutions, the DeepHealth OS, is a cloud-native operating system that orchestrates all
data to drive value across the enterprise. DeepHealth aims to elevate the radiologist’s
role beyond radiology and across the entire care pathway. It empowers all users across
the care continuum with personalized workflows to make work easier and more
meaningful.
DeepHealth leverages advanced AI technologies in breast, lung, and prostate health,
and operational efficiencies to create end-to-end efficiency across the enterprise.
www.deephealth.com
Role Summary
The Machine Learning Engineer will contribute to developing and productionizing
artificial intelligence (AI) products. This includes AI model development, data
management and software engineering tasks, and working with a team of software
engineering, clinical, and regulatory specialists to deliver these models to clinical care.
Essential Duties and Responsibilities
- Driving development of new machine learning models and maintaining and
improving current AI models.
- Working with clinical team to assess AI model performance.
- Curating and analyzing large medical imaging datasets.
Writing production-level code to turn AI models into robust
products.Competencies
- Medical imaging model development and productization.
- Technical and clinical validation of imaging models.
- Knowledge of algorithms and model development techniques.
- Attention to detail.
Strong presenter of ideas and work
Qualifications:
MSc / PhD in Machine Learning field, Computer Science or related technical
discipline.
- Experience (> 5 years) with deep learning tasks including applying them to
medical imaging.
- Experience (> 5 years) with deep learning libraries.
- Strong programming skills in Python and its scientific computing libraries.
- Experience with analyzing large datasets.
- Experience with version control and Git.
- Valid EU work permit.
Preferred
- Experience working on FDA and CE regulated products.
- Knowledge and experience in cloud infrastructure (e.g., Google Cloud, AWS,
etc.).
- Experience with a container platform (Docker, Kubernetes).
- Experience with software development lifecycles and best practices.
