AI Intern

Job description

About CertifyOS CertifyOS is building the data infrastructure that powers modern healthcare. Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem. Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale. Our vision is simple: One API. One provider ID. Frictionless provider data. We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients. About the Role: As an Intern Machine Learning Engineer on a 6-month contract, you will help build, test, and deploy ML-powered services on our provider data platform. This is not a pure research role; the focus is on strong software engineering, testing, and robust evaluation rather than novel model architectures. You will own features end-to-end by collaborating with stakeholders, implementing production-ready code, designing evaluation pipelines, and deploying services on Google Cloud Platform (GCP). This is a fully remote position. What You’ll Do: Design, implement, and maintain ML-driven services and data workflows in Python. Apply software engineering best practices, including clean code, testing (unit and integration), code reviews, CI/CD, observability, and documentation. Build and maintain evaluation pipelines and metrics to measure model and system performance in production-like environments. Deploy and operate ML services on GCP, including tools such as Cloud Run, GKE, Cloud Functions, Pub/Sub, BigQuery, and Cloud Storage. Troubleshoot and improve existing ML services with a focus on reliability, latency, and correctness. Collaborate proactively with internal stakeholders across product, operations, engineering, and data teams to clarify requirements and iterate on solutions. Communicate clearly about trade-offs, risks, timelines, and results to both technical and non-technical audiences. What