• Creating new ways to apply deep learning to solve everyday clinical problems • Image acquisition • Reconstruction • Post-processing and analysis • Designing and implementing algorithms with real-time feedback for the technician and patient • Educating team members with your expertise while also learning from their experience • Contributing to academic publications and attending conferences such as RSNA, ISMRM, SCMR, NeurIPS, CVPR

• Lead engineering of human-in-the-loop digital avatars for behavioral health support • Collaborate with internal teams and stakeholders to deliver machine learning applications • Develop web-based applications and data infrastructures • Mentor and guide junior engineers

• Design and create environments for data scientists to build models and manipulate data • Work within customer systems to extract data and place it within an analytical environment • Learn and understand customer technology environments and systems • Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop • Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models • Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle • Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment • Ensure the quality of the delivered product

• Design and build ELT pipelines for data processing and analysis. • Construct MLOps pipelines for automated retraining and validation of models. • Implement CI/CD pipelines for deploying models and ML services. • Create services for monitoring ML models in production.