Machine Learning Engineer

Overview

The Machine Learning Engineer owns the intelligence layer that makes Evergreen's agents clinically meaningful rather than simple rule executors. This person designs, trains, validates, and monitors the ML models that power the Markov state transition logic, clinical decision support, patient risk stratification, adherence prediction, and dosage optimization. They work at the intersection of clinical domain knowledge and applied ML — building models that must be explainable to providers, safe for patients, and auditable by regulators. As the architecture matures, this role expands into the predictive analytics layer (Amazon Forecast DeepAR+ for churn risk, adherence forecasting) and the evaluation frameworks that determine whether agent recommendations meet clinical safety thresholds. This is not a research role — it is an applied ML engineering role where every model must ship, every prediction must be defensible, and every failure mode must be anticipated.

Job Activities and Responsibilities

The responsibilities listed below are subject to change.

• Build the evaluation and validation framework for all agent-driven clinical recommendations — including safety guardrails, confidence thresholds, and human-in-the-loop escalation triggers for the Clinical Protocol Agent
• Develop patient risk stratification models for adherence prediction, adverse event likelihood, dosage titration optimization, and churn/dropout risk using clinical, behavioral, and engagement signals
• Implement and manage the predictive analytics pipeline on AWS — Amazon Forecast (DeepAR+) for time-series clinical predictions, S3 Vectors for embedding-based patient similarity and retrieval, and Bedrock for agent inference
• Design and build the RAG architecture that grounds agent responses in clinical protocols, formulary data
• Own model lifecycle management — training pipelines, feature stores, model versioning, A/B testing, drift detection, and retraining triggers in...