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
The Lead Machine Learning Engineer is responsible for developing and productionizing artificial intelligence (AI) products. This includes both AI model development 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
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Developing new machine learning / AI models.
- Maintaining and improving current AI models.
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Working with clinical team to assess AI model performance.
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Curating and analyzing large medical imaging datasets.
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Writing production-level code to turn AI models into robust products.
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Maintaining state-of-the-art knowledge of medical imaging and model development.
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Medical imaging model development and productization.
- Technical and clinical validation of medical imaging models.
- Delivering results through teamwork including offering constructive challenge where required.
- Knowledge of state-of-the-art algorithms and model development techniques.
Ability to balance strong attention to detail with making progress.
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Minimum Qualifications, Education and Experience
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PhD in Machine Learning field or equivalent experience.
- Experience (> 5 years) with deep learning tasks applied to medical imaging.
- Track record of developing and validating novel models.
- Strong programming skills in Python and its scientific computing libraries (pandas, numpy, etc.).
- Experience (> 5 years) with deep learning libraries (PyTorch, TensorFlow, etc.).
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Experience with analyzing large datasets.
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Strong publication record.
- Experience working on FDA regulated products.
- Experience supervising a machine learning team.
- Knowledge and experience in cloud infrastructure (Google Cloud, AWS, etc.).
- Experience with a container platform (Docker, Kubernetes).
- Experience working with large-scale medical data.
Experience with software development lifecycles and best practices.
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Join a dynamic team with expertise in various fields.
- Collaborative and agile work environment.
- Continuous learning opportunities to enhance your professional skills.
- Fully remote working environment with flexibility in work hours.
- A salary in line with job level and experience.