Circadia Health

Senior ML Ops Engineer

Circadia Health  •  El Segundo, CA (Onsite)  •  4 hours ago
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Job Description

About Circadia Health

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

  • Native predictive models that detect 85% of preventable adverse events several days in advance

  • Enterprise integrations that operationalize predictions directly inside EHR, care-coordination, billing, and compliance workflows

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 and headquartered in El Segundo, CA.

Why this role exists

Our models decide whether a care team walks into a room tonight. They train on 70,000 years of continuous vital signs joined to clinical records from more than 400,000 unique patients. When one of them degrades it does not surface as an error rate. It surfaces as a patient who deteriorated and nobody was alerted, which means the platform that trains, ships, and watches those models carries real clinical weight. You will own it: the pipelines, the path to production, and the monitoring that catches degradation before a clinician would.

What you'll own

  • Pipeline orchestration. Training, evaluation, and deployment workflows in Airflow, with automated retraining, promotion, and failure recovery.

  • Deployment and release. Models onto our platform on AWS including Batch, with versioning and rollback through MLflow, maturing toward shadow and canary releases.

  • Tracking and lineage MLflow registry, conventions for artifacts and metadata, and dataset versioning so training runs are reproducible.

  • Monitoring and drift. Drift, prediction quality, and degradation alerting on models where degradation is clinically consequential.

  • ML compute and cost. AWS compute for training and inference, infrastructure-as-code, and cost optimization.

  • Hands-on model work. Contributing to model development alongside the ML engineering team, as a secondary focus behind the platform.

  • Compliance. HIPAA and SOC 2 across pipelines, with sound PHI handling in training data, artifacts, and outputs.

  • Required Qualifications

    • 4+ years in MLOps, ML engineering, DevOps, or a closely related infrastructure role
    • Strong Python for pipeline development, tooling, and automation
    • Hands-on Airflow, and a model registry such as MLflow
    • Deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch)
    • Containerization, infrastructure-as-code, SQL, and Snowflake
    • Building monitoring and alerting for production systems
    • Enough model development experience to contribute alongside ML engineers

    Preferred

    • Model serving frameworks or data versioning tools
    • Healthcare, medical devices, or clinical data systems
    • Significant open source, systems that outlived your tenure, or a high-bar engineering background

    Circadia Health is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other status protected by law.

    Circadia Health

    About Circadia Health

    Powered by the world’s first FDA-cleared truly contactless patient monitoring system, Circadia improves clinical outcomes through early detection. The technology monitors a patient’s respiratory rate from up to 8 feet away without anything on the bed or body. Clinicians use this data to identify medical events such as Congestive Heart Failure, COPD Exacerbations, Pneumonia, Sepsis, UTIs, and Falls — several hours and days in advance. A team of virtual nurses review data from the device and analyze trends in the patient’s electronic health record to deliver personalized, predictive risk alerts 24/7/365.

    Industry
    Manufacturing & Production
    Company Size
    51-200 employees
    Headquarters
    Los Angeles, California
    Year Founded
    2016
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