Principal Machine Learning Engineer

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

Circadia’s core advantage is deep, production-grade predictive modeling on messy, high-stakes healthcare data—not demos, not dashboards, but models that materially change clinical and financial outcomes.

As a Principal ML Engineer, you will own the full lifecycle of native ML models—from feature engineering and model training to validation, monitoring, and continuous improvement in production. Your work will directly power risk stratification, early-warning systems, and agentic workflows used by clinicians and operators every day.

This is a role for someone who loves tabular data, time-series signals, causal nuance, and shipping models that actually work in the wild.

What You’ll Do

Build & Own Core Predictive Models

• Design, train, and iterate on XGBoost, LightGBM, CatBoost, and other native ML models for risk prediction, classification, and regression
• Develop time-series and longitudinal models using vitals, motion, utilization, and claims-adjacent data
• Own feature pipelines spanning raw sensor outputs, clinical indicators, utilization patterns, and derived signals

End-to-End Model Lifecycle

• Take models from research → validation → production → monitoring
• Define labeling strategies, handle missingness, censoring, and class imbalance
• Establish retraining cadences, drift detection, and performance guardrails

Clinical & Operational Rigor

• Partner with clinicians to ensure models are clinically interpretable, safe, and actionable
• Produce explainability artifacts (e.g., SHAP, feature attribution) suitable for audits, clinicians, and enterprise buyers
• Balance sensitivity/specificity trade-offs in real operational contexts (false positives matter)

Production & Platform Integration

• Collaborate with platform engineers to deploy models via APIs and batch pipelines
• Optimize inference latency and cost at scale
• Ensure models integrate cleanly into downstream agentic and workflow systems

Measurement & Outcomes

• Define and track real-world impact metrics (avoidable hospitalizations, LOS, cost reduction, staff efficiency)
• Run offline validation, shadow deployments, and post-deployment analyses
• Continuously improve models based on live outcomes, not just offline AUC

Must-Have Qualifications

• 5–10 years of experience building and shipping native ML models in production environments
• Deep hands-on experience with XGBoost (required) and at least one of LightGBM / CatBoost
• Strong foundation in statistics, ML fundamentals, and model evaluation
• Proven experience with tabular and/or time-series healthcare-like data (messy, sparse, biased, incomplete)
• Advanced Python skills; comfortable with NumPy, pandas, scikit-learn, and ML tooling
• Experience owning models end-to-end, not just experimentation notebooks
• Clear communicator who can explain model behavior and trade-offs to non-ML stakeholders
• High ownership mindset — you’ve carried models through failures, audits, and real-world edge cases

Nice-to-Haves

• Experience in healthcare, insurance, fintech, or other regulated, high-signal domains
• Familiarity with survival analysis, hazard models, or early-warning systems
• Experience with sensor data, physiological signals, or remote monitoring
• Comfort working alongside LLM-based systems (even if you don’t build them)
• Prior startup experience where models directly impacted revenue or operations
• Publications, talks, or deep technical writing on applied ML

You’ll Thrive Here If…

• You believe AUC is table stakes — outcomes are what matter
• You enjoy arguing about feature leakage, label bias, and deployment reality
• You want your models used by clinicians
• You move fast but demand rigor when patient impact is involved
• You take pride in models that hold up under scrutiny

Compensation & Perks

• Base Salary: $160k – $260k base salary meaningful employee stock options
• Benefits: 100% company-paid medical, dental, vision; 401(k) with match; generous PTO
• Workspace: El Segundo HQ with rooftop views, espresso bar, and weekly team lunches
• Impact: Your models will influence care decisions for tens of thousands of seniors every day