Senior Machine Learning Engineer

Circadiahealth; El Segundo (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Circadia Health in El Segundo, CA is hiring a senior ML Engineer to own end-to-end models that predict clinical events from physiological signals and EHR data. You will design, train, and evaluate models, define targets with clinical teams, and implement robust evaluation and deployment pipelines.

You will ensure model performance, calibration, and fairness across facilities, and communicate results to non-technical stakeholders while advancing regulatory-ready evidence for deployment in care

Qualifications

• 5+ years building ML models that reached production and informed decisions.
• Strong Python and deep learning frameworks with fluency in classical ML.
• Experience with time-series or sequential data.
• Evaluation practices for calibration, imbalance, and leakage.
• Experience building evaluation, backtesting, or model regression infra.
• Strong SQL and working with production data.
• Ability to present model behavior to non-technical stakeholders.
• Healthcare ML, EHR data, physiological signals, or early-warning systems.

Responsibilities

• Model development: design, train, and evaluate clinical prediction models with feature engineering on time-series data and EHR context.
• Define targets with clinical teams and build adjudication workflows.
• Build eval harnesses, backtesting, and regression suites for safe deployment.
• Assess sensitivity, specificity, lead time, and alert burden for care teams.
• Identify performance variations across facilities and demographics.
• Ship with ML Ops support on serving and deployment; support validation studies.

Skills

Python

Deep learning

Time-series

SQL

Model evaluation

Stakeholder comms

Tools

ML Ops

Backtesting

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.

At most companies the ML engineer supports the product. Here the model is the product, and its accuracy is the ceiling on what the whole platform can deliver. What we sell is the judgment layer on top of a corpus most teams will never get access to: 70,000 years of continuous vital signs joined to clinical records from more than 400,000 unique patients. You will own the models built on it end to end: what they predict, how they are evaluated, where the threshold sits, and when they ship. Ground truth is retrospective chart review, so your labels are imperfect and you will need to know exactly how.

Responsibilities

• 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.

Requirements

• 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

• 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.