Machine Learning Scientist

Website Arterys

Medical Imaging and Cloud AI

As a member of the Machine Learning team at Arterys, you will be responsible for developing novel deep learning-based approaches to automate many aspects of medicine, including radiology, disease diagnosis, and preventative medicine. You’ll spend time prototyping novel algorithms, translating them into production-level code, and integrating them into our state-of-the-art cloud-based software. You’ll develop elegant and efficient solutions to real-world problems and the work you do will have significant impact on improving the efficiency and accuracy of radiological image interpretation and diagnosis.

Continued professional growth is important to us at Arterys. You’ll be formally mentored by a more experienced team member while you are getting settled in to your first project. We also encourage spending time on projects that interest you, even if those projects are not immediate product needs. We often meet as a team to discuss problem approaches and we hold a regular journal club to discuss recent deep learning publications. We support travel to engineering, scientific, and clinical conferences, and we regularly publish our research in peer-reviewed journals. Expect to learn new things on a daily basis and become part of a growing community of medical machine learning practitioners.

– Ownership of machine learning projects from concept to execution
– Development of state-of-the-art medical machine learning models
– Cross-functional collaboration to create amazing products that our users love


At least one of:
– M.S. or Ph.D. in computer science or a related engineering discipline and 1+ years of production software development experience, or
– 3+ years of production software development experience
– Strong background in machine learning and deep learning, particularly convolutional neural networks
– Strong Python experience
– Fluency with modern deep learning packages, particularly TensorFlow and Keras
– Experience working on efficient, well tested, easily maintainable code

Nice to Haves
– Experience with medical image data, particularly the DICOM standard
– Experience with handling and processing large datasets (1 TB and larger)
– Experience with crowdsourcing platforms (Mechanical Turk, CrowdFlower, etc.)

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