Michael Chen

Led end-to-end development of cloud-native healthcare applications, AI-powered full-stack experiences, and reusable platform capabilities. Built and improved HIPAA-eligible platform applications across React/TypeScript, backend services, APIs, and AWS infrastructure supporting healthcare data workloads at petabyte scale. Developed healthcare APIs and data services processing billions of transactions annually with sub-second response times across hundreds of customers. Integrated generative AI using Amazon Bedrock and healthcare data services to support clinical search, patient-context retrieval, and AI-generated patient summaries. Developed FHIR-based data and interoperability capabilities supporting large healthcare migrations, including workloads reaching billions of FHIR resources and thousands of transactions per second. Improved platform deployment, monitoring, and infrastructure automation for reliable production releases; collaborated with product, security, and clinical teams t

Built and scaled machine learning platform services, distributed backend systems, and cloud-native developer infrastructure to help product teams productionize data-driven capabilities. Created shared backend and ML platform services supporting development, training, and deployment of ML workloads on AWS. Contributed to scalable inference infrastructure as SageMaker grew to serve tens of thousands of customers in healthcare and other industries. Developed distributed services and APIs used in production ML workflows with a focus on availability, scalability, and operational simplicity. Worked on ML inference and deployment capabilities supporting frameworks including PyTorch, TensorFlow, ONNX, MXNet, XGBoost, and Keras across cloud and edge environments. Improved model serving efficiency as SageMaker Neo expanded, delivering up to 25x inference performance improvements across supported workloads, and built automation/tooling for deployment, monitoring, and infrastructure provisioning t

Built and scaled cloud observability infrastructure, high-throughput telemetry ingestion and processing services, and developer tooling. Helped build DataDog’s cloud monitoring platform as it grew to monitor over 100,000 servers and process over 25 billion data points per day. Developed telemetry ingestion and processing services supporting monitoring data from large distributed cloud environments. Worked on backend systems supporting integrations across nearly 100 infrastructure, data, application, and cloud technologies; built services and internal tooling to help engineering and operations teams investigate performance issues and troubleshoot production incidents. Improved monitoring and operational workflows for a customer base that grew to thousands of companies; collaborated with engineers on reliability, performance, debugging, and scaling across AWS, Microsoft Azure, Google Cloud Platform, OpenStack, and other cloud environments.