Overview
 

Healthcare delivery involves hundreds of repetitive, manual, error-prone tasks that account for over $1 trillion in administrative costs per year. Notable’s platform unifies artificial intelligence, robotic process automation, design, and no-code configurability to automate these workflows across the continuum of care – improving patient outcomes and reducing costs. Our mission is to enrich every patient interaction through modern digital experiences and intelligent automation. Leading healthcare organizations like CommonSpirit Health and Intermountain rely on our platform to provide a delightful omni-channel experience, deliver care at scale, reduce clinician documentation burden, and drive efficiency.

As an ML/AI engineer at Notable, you’ll work on developing and deploying machine learning models which provide the intelligence powering robotic processes that underpin critical healthcare workflow automation. The types of AI tasks  range from natural language understanding, including document classification and information extraction , to computer vision for robotic process automation, such as object detection and optical character recognition. You’ll work closely with the product development team to define ML systems tailored to our needs, help us build the necessary data and computing infrastructure, and ship new solutions to enable intelligent automation.

Our interview process is meant to be representative of the kinds of work we will do together day-to-day and week-to-week.  We look for smart people who can implement well crafted solutions to complex problems in a fast paced environment, and who can help us attract more smart people.  We don't expect you to have experience with our stack (Google Cloud Platform, Python, Tensorflow, kubeflow, PyTorch, FastAPI, Kubernetes), but we do look for demonstrated mastery of your chosen development stack and a desire to learn new technologies.
 
 
Requirements
  • Excellent problem solving, coding, and debugging skills
  • Strong mathematical background (MS or PhD in CS, Physics, Biology, or similar)
  • Experience implementing deep learning models on unstructured data
  • Experience deploying and maintaining deep learning models in production
  • Experience building and maintaining data processing and machine learning pipelines
  • Experience writing maintainable, well tested, reliable code
  • Experience in a fast-paced, collaborative environment
  • Valid US work authorization

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