Job Title

Who We Are:CurieDx is a Johns Hopkins–affiliated digital health startup building a “lab in your pocket.” Our platform uses smartphone images, digital biomarkers, and AI to detect infections like strep throat, influenza, and UTI, helping patients and clinicians make faster, evidence-based decisions without swabs, labs, or long clinic visits.We are moving deep learning models from research into real-world clinical deployment. This role is critical to making that happen reliably, securely, and at scale.We're a small, fast team where your work ships to production and directly impacts patient care. This is a role where you're building the data pipelies ML infrastructure from the ground up.Role DescriptionWe are hiring an MLOps Engineer who can own the data pipelines that power our AI platform, and infrastructure.What you'll own:ML Pipeline EngineeringBuild and maintain AWS SageMaker pipelines for training, validation, and deploymentImplement experiment tracking and model versioningAutomate retraining workflowsData Engineering for MLWrite Python scripts to ingest, clean, transform, and validate metadata datasetsBuild preprocessing and augmentation pipelines for image and other data formatsStructure data so ML engineers can immediately begin model developmentMaintain dataset versioning and lineageInfrastructure & Cloud ArchitectureDesign AWS architecture for GPU training workloadsManage S3 data storage, IAM roles, networking, and security configurationsOptimize cost and compute efficiencyBuild monitoring and logging systems for production ML servicesProduction DeploymentContainerize and deploy models for inferenceImplement performance monitoring and drift detectionImprove reliability and observability of deployed ML systemsWhat We're Looking For:3+ years of experience in MLOps, ML engineering, or production ML system deploymentsHands-on experience building data pipelines for image/video preprocessing, augmentation, and annotation workflowsDeep AWS expertise with hands-on experience in SageMaker, EC2 (GPU instances), S3, Lambda, and broader AWS ecosystem for ML workloads Experience with CI/CD pipelines, containerization (Docker), and orchestration tools (Airflow, Step Functions) Familiarity with annotation tools and data labeling workflows Must be comfortable operating in lean environments - scrappy, resourceful, and action-oriented Why Join CurieDxBacked by Johns Hopkins, Microsoft, National Institutes of Health, National Science Foundation, and BARDAReal-world deployment of AI in healthcareLean, fast-moving startup environmentOpportunity to build foundational ML infrastructure from the ground upSummaryIf you're passionate about creating meaningful impact and want to join a team that values collaboration and innovation, we'd love to hear from you.  CurieDx is an equal opportunity employer and values diversity. All qualified applicants will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.