Machine Learning Engineer II (Applied Research & Model Development)

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

The Opportunity

We are seeking Machine Learning Engineers (Applied Research & Model Development) to tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services. You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals. Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations. You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes. You will design, develop, and deploy machine learning models for research and product development projects. You will collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions. You will contribute to experimental design and analysis, including ideation, documentation, and reporting. You will participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities). You will improve ML pipelines and infrastructure in partnership with and platform teams. You will publish and present scientific work, supporting abstracts, manuscripts, and conference contributions.