Machine Learning Engineer (ML Ops & Pipelines)

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 Description We are hiring an MLOps Engineer who can own the data pipelines that power our AI platform, and infrastructure. What you'll own: ML Pipeline Engineering Build and maintain AWS SageMaker pipelines for training, validation, and deployment Implement experiment tracking and model versioning Automate retraining workflows Data Engineering for ML Write Python scripts to ingest, clean, transform, and validate metadata datasets Build preprocessing and augmentation pipelines for image and other data formats Structure data so ML engineers can immediately begin model development Maintain dataset versioning and lineage Infrastructure & Cloud Architecture Design AWS architecture for GPU training workloads Manage S3 data storage, IAM roles, networking, and security configurations Optimize cost and compute efficiency Build monitoring and logging systems for production ML services Production Deployment Containerize and deploy models for inference Implement performance monitoring and drift detection Improve reliability and observability of deployed ML systems What We're Looking For: 3+ years of experience in MLOps, ML engineering, or production