Senior Machine Learning Engineer, VideaHealth

• We’re looking for a hands-on ML expert who’s excited to build, ship, and own real-world products
• If you're a builder with a passion for impact, eager to grow fast and help define the future of AI in healthcare — we’d love to meet you
• Minimum of 4 years of hands-on development building deep learning models
• Experience with at least one deep learning framework (e.g. PyTorch, TF, Keras)
• Strong software development skills including testing (Python) and leading development in a shared codebase
• Strong communication and collaboration skills

Desirable

• We are looking for a Senior Machine Learning Engineer to join our growing ML team
• This is an exciting opportunity to contribute and grow your technical skills by building end to end highly impactful software medical devices that will ultimately impact the lives of millions of people
• In this role, you’ll take full ownership of high-impact projects and design robust infrastructure that powers our clinical applications, from computer vision models to decision support systems
• You’ll work side-by-side with our leadership team and collaborate across a dynamic, tight-knit team that thrives on innovation and execution
• You’ll have the opportunity to work at the cutting edge of AI and healthcare, in close collaboration with renowned advisors from MIT and Harvard, and alongside researchers, physicians, and regulatory experts, including those with deep FDA experience
• Own the design and implementation of infrastructure for reliable and highly performant deep learning and analytics pipelines with the main focus on the detection and classification of dental conditions from images
• Research, prototype, and deploy production-grade computer vision models (e.g., CNNs, transformers)
• Play a critical role in defining and executing processes for managing large amounts of data, defining data sampling approaches and performance evaluations
• Write traceable, testable, and reproducible code to support FDA/CE-cleared ML workflows. Collaborate on documentation for 510(k) submissions
• Design model monitoring tools for drift detection, explainability, and performance in production
• Partner with clinical experts and software engineers to ensure models are safe, effective, and usable in real-world healthcare environments