Job Description
About Cleerly We’re Cleerly – a healthcare company that’s revolutionizing how heart disease is diagnosed, treated, and tracked. We were founded in 2017 by one of the world’s leading cardiologists and are a growing team of world-class engineering, operations, medical affairs, marketing, and sales leaders. We raised $223M in Series C funding in 2022 which has enabled rapid growth and continued support of our mission. In December 2024 we received an additional $106M in a Series C extension funding. Most of our teams work remotely and have access to our offices in Denver, Colorado, New, York, New York, Dallas, Texas, and Lisbon, Portugal with some roles requiring you to be on-site in a location. Cleerly has created a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions with the goal of helping prevent heart attacks. Our technology goes beyond traditional measures of heart disease by enabling comprehensive quantification and characterization of atherosclerosis, or plaque buildup, in each of the heart arteries. Cleerly’s solutions are supported by more than a decade of performing some of the world’s largest clinical trials to identify important findings beyond symptoms that increase a person’s risk of heart attacks. At Cleerly, we collaborate digitally and use a wide variety of systems. Our people use Google Workspace (GMail, Drive, Docs, Sheets, Slides), Slack, Confluence/Jira, and Zoom Video, prior experience in these areas is a plus. Role or department specific technology needs may vary and will be listed as requirements in the job description. While we are mostly a remote company, travel is required for some team meetings and cross function projects typically once per month or once per quarter, for some roles like sales or external facing roles travel could be up to 90% of the time. Cleerly is committed to providing safe and effective medical software that meets customer needs and our intended use. The adherence to all applicable regulatory and statutory requirements establishes a clear framework for setting measurable quality objectives. Our commitment to continually improving our products and processes proactively manages risks, ensuring ongoing compliance throughout the entire software lifecycle. Understanding this role's relevance and importance is critical to achieving Cleerly's quality objectives. About The Opportunity We are seeking an experienced Staff Machine Learning Engineer to architect, scale, and advance our machine learning platforms that bridge AI innovation and production in regulated healthcare. In this high-impact role, you will define and implement core platform capabilities, enabling scalable, secure, and compliant deployment of ML models that directly impact the care pathway for heart disease diagnosis and prognosis. You will tackle complex engineering challenges across end-to-end ML pipelines, ensuring reproducibility, efficiency, and compliance while driving the technical evolution of the platform. About The Team The AI Software Engineering team translates advanced ML models into production-ready, scalable solutions that directly impact the care pathway for heart disease diagnosis and prognosis. Working closely with AI scientists, software engineers, and regulatory teams, the team ensures models and ML workflows integrate seamlessly into clinical and product systems while maintaining reproducibility, compliance, and high performance. The team drives continuous improvements in efficiency, throughput, and infrastructure utilization, delivering reliable, scalable AI services that advance the accuracy and impact of Cleerly’s regulated products. Responsibilities Architect and develop scalable AI/ML platforms and end-to-end pipelines, covering data ingestion, preprocessing, model training, evaluation, deployment, monitoring, drift detection, and automated retraining, while ensuring reproducibility, compliance with FDA/HIPAA, and alignment with organizational and regulatory goals. Optimize and operationalize production ML systems, including monitoring, drift detection, automated retraining, and workflow execution, to achieve high performance, reliability, scalability, and regulatory adherence. Evolve the ML stack through integration and refinement of frameworks, libraries, and infrastructure, improving system efficiency, maintainability, and the ability to support clinical ML workflows. Ensure operational readiness of ML pipelines and platforms, verifying data quality, throughput, reproducibility, and compliance across production workflows. Drive improvements in processes, tooling, and collaboration to streamline the transition of ML models from research to production, enhancing efficiency, reproducibility, and compliance across the platform.