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 Junior 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
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Contribute to developing new machine learning models and to maintaining and improving current AI models.
- 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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Computer vision / medical imaging model development and productization.
- Technical and clinical validation of imaging models.
- Knowledge of algorithms and model development techniques.
Attention to detail.
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Minimum Qualifications, Education and Experience
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BSc / MSc in Machine Learning field, Computer Science or related technical discipline.
- Experience (> 2 years) with deep learning tasks applied to medical imaging.
- Strong programming skills in Python and its scientific computing libraries (pandas, numpy, etc.).
- Experience (>2 years) with version control and Git.
- Experience (> 2 years) with deep learning libraries (PyTorch, TensorFlow, etc.).
Experience with analyzing large datasets.
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Experience working on FDA 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.
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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.