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Senior Machine Learning Engineer

Circadia Health · El Segundo

Extracted from the posting

Base
$150,000 – $240,000
Remote
On-site
Stack
Python, SQL

Senior Machine Learning Engineer owning clinical prediction models end to end, including feature engineering on physiological time series and EHR data, labeling, evaluation, and threshold selection. Work includes backtesting and regression infrastructure, subgroup robustness analysis, and support for production deployment and regulatory validation. Requires Python, deep learning frameworks, and SQL.

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

    What you'll own

    • Model development. Design, train, and evaluate clinical prediction models, with feature engineering across physiological time series and structured EHR context.
    • Labels and ground truth. Define what you are actually predicting with clinical teams, build adjudication workflows, and understand the noise in your targets.
    • Evaluation and testing infrastructure. Build the eval harnesses, backtesting, and regression suites that let us ship new model versions and new configurations with confidence, including how flagging behaves and whether explanations hold up.
    • Clinical evaluation. Sensitivity, specificity, lead time, and alert burden as the care team experiences them. Threshold selection is a clinical decision as much as a statistical one.
    • Robustness. Find where performance varies across facilities, settings, and demographics, and quantify it.
    • Production and evidence. Ship with ML Ops support on serving and deployment, monitor real-world performance, and contribute to validation studies and regulatory submissions.

    Required Qualifications

    • 5+ years building ML models that reached production and were used for real decisions
    • Strong Python and modern deep learning frameworks, plus fluency in classical ML
    • Experience with time-series or sequential data
    • Evaluation practice covering calibration, class imbalance, and temporal leakage
    • Experience building evaluation, backtesting, or model regression infrastructure
    • Strong SQL and experience with production data
    • Experience presenting model behavior and limitations to non-technical stakeholders

    Preferred

    • Healthcare ML, clinical prediction, EHR data, physiological signals, or early-warning systems
    • Model validation supporting regulatory submission, or subgroup analysis in a clinical setting
    • First-author publications, significant open source, competition results, or a high-bar research or 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.

    Compensation: USD 150,000 – 240,000 per year salary

    Technologies

    PythonSQL
    Circadia Health
    Circadia Health

    Job details

    TypeFull-time
    Work ModeOn-site
    LevelSenior
    Location🇺🇸El Segundo, CA
    Salary$150,000 - $240,000
    PostedNov 06, 2026
    Apply Now

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