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 ML system deployments Hands-on experience building data pipelines for image/video preprocessing, augmentation, and annotation workflows Deep 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 CurieDx

Backed by Johns Hopkins, Microsoft, National Institutes of Health, National Science Foundation, and BARDA Real-world deployment of AI in healthcare Lean, fast-moving startup environment Opportunity to build foundational ML infrastructure from the ground up

Summary

If 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.