Machine Learning Engineer

Circadia Health is a growth‑stage healthcare AI company on a mission to prevent avoidable hospitalizations and transform senior‑care operations. Our Circadia Intelligence Platform combines:

• Contactless sensing that monitors respiration and motion with medical‑grade accuracy
• Predictive analytics & agentic AI workflows that detect 85 % of preventable rehospitalizations ~11 days in advance
• Enterprise integrations that embed insights directly into EHR, care‑coordination, billing, and compliance systems

Today our technology touches 40,000+ post‑acute patients daily across skilled‑nursing, home‑health, and home‑care networks. We are backed by leading healthcare and AI investors like Khosla Ventures, Village Global, Headline, Eric Yuan (CEO of Zoom), and others.

Position Overview

As an ML Ops Engineer at Circadia Health, you will own the infrastructure and operational lifecycle of the machine learning systems that power our clinical monitoring platform. You will build and maintain the production ML pipelines, deployment infrastructure, and monitoring systems that enable Circadia's predictive models to identify early signs of clinical deterioration. Reporting to the Principal ML Engineer, you will work across ML, backend, data, and clinical teams to ensure models are reliably trained, versioned, deployed, and monitored in both cloud and edge environments. You will be a key driver in elevating Circadia's ML practice – from reproducibility and experiment tracking to CI/CD for models and operational observability. This is a high-ownership role at a lean company where production reliability, rapid iteration, and pragmatic engineering are essential. Your work will directly impact patient outcomes by ensuring our predictive models are always running, always accurate, and always improving.

Key Responsibilities

ML Pipeline Orchestration & Automation

• Own and extend Circadia’s ML pipeline orchestration using Apache Airflow, including training, evaluation, and deployment workflows.
• Build and maintain automated pipelines for model retraining, validation, and promotion across development, staging, and production environments.
• Implement pipeline monitoring, alerting, and failure recovery to eliminate silent failures and ensure operational reliability.
• Design pipeline architectures that support rapid experimentation while enforcing production-grade reproducibility.

Model Deployment & Serving

• Deploy and manage ML models on AWS infrastructure (e.g. AWS Batch for batch inference workloads).
• Support deployment of models to edge devices, including Circadia’s clinical monitoring hardware, working with firmware and embedded engineering teams as needed.
• Manage model versioning, promotion, and rollback workflows through the MLflow model registry.
• Evaluate and implement strategies for safe model rollouts (e.g. shadow deployments, canary releases) as the platform matures.

Experiment Tracking & Model Registry

• Maintain and improve the MLflow-based experiment tracking and model registry.