Description: Arterys is working on applying artificial intelligence and deep learning to healthcare to impact the lives of millions of people. We are looking for passionate and talented computer vision experts to advance the field of medical imaging using deep learning.
Much of clinical radiology consists of clinicians performing tedious tasks that would greatly benefit from automation, such as early detection of cancerous lesions, segmenting anatomical regions and characterizing pathologies. Arterys’ goal is to use the latest machine learning technology to automate these tasks and help radiologists work more efficiently and improve outcomes.
As an Image Scientist at Arterys, you will be part of the core team responsible for developing novel deep learning-based approaches to automate many aspects of radiology across many different imaging modalities (CT, MRI, etc.) and organ systems (heart, brain, lung, etc.). Algorithms that you develop will save radiologists valuable time and increase the accuracy and consistency of radiological analysis while improving patient outcomes. You’ll spend time discussing important clinical problems with key physicians, prototyping novel algorithms and helping to productize and integrate them into our state-of-the-art cloud-based radiological viewing software. You’ll develop beautiful tools from messy, real-world data sets and you’ll help improve the lives of millions of patients.
- Strong background in machine learning and deep learning, particularly convolutional neural networks
- M.S. or Ph.D. in computer science or a related engineering discipline
- Excellent software development skills
- Experience with one or more general purpose programming languages, particularly Python and C++
Nice to Have
- 2+ years industry experience
- Experience in the following areas:
- Image processing, particularly with OpenCV
- Medical images and the DICOM format
- Amazon Web Services, particularly EC2 and S3
- Crowdsourcing (e.g., Mechanical Turk)
Application Instructions: Please e-mail your resume and a short cover letter, including a description of your experience with convolutional neural networks, to email@example.com