Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop the evaluation and validation framework for all agent-driven clinical recommendations, which includes safety guardrails, confidence thresholds, and human-in-the-loop escalation triggers for the Clinical Protocol Agent.

• Create patient risk stratification models to predict adherence, assess the likelihood of adverse events, optimize dosage titration, and evaluate churn/dropout risks using clinical, behavioral, and engagement signals.

• Establish and oversee the predictive analytics pipeline on AWS, utilizing Amazon Forecast (DeepAR+) for time-series clinical predictions, S3 Vectors for embedding-based patient similarity and retrieval, and Bedrock for agent inference.

• Design and construct the RAG architecture to ensure agent responses are grounded in clinical protocols and formulary data.

• Manage the model lifecycle, which includes training pipelines, feature stores, model versioning, A/B testing, drift detection, and retraining triggers in a production environment.

• Develop explainability layers for clinical recommendations.

• Collaborate with the clinical team to convert clinical protocols and pharmacy domain knowledge into model features, training labels, and validation criteria.

• Set up model monitoring and alerting systems, including prediction quality dashboards, distribution shift detection, and automated alerts for when model performance falls below clinical safety thresholds.

• Partner with DevOps/AgentOps colleagues to guarantee that all ML decisions are logged, reproducible, and auditable for regulatory review.

⛳️ Requirements

• Over 6 years of experience in ML engineering or applied data science, with a minimum of 3 years spent deploying ML models in production within the healthcare, biotech, or clinical sectors.

• Direct experience in developing clinical decision support, risk stratification, or patient outcome prediction models, with a solid understanding of the regulatory and ethical implications of ML in healthcare.

• Practical experience with LLM-based agent systems, having built or significantly contributed to a system where an LLM makes critical decisions with safety guardrails in place.

• Proven ability to collaborate with clinical domain experts, such as physicians, pharmacists, and clinical researchers, to translate domain knowledge into model design choices.

• Experience in HIPAA-regulated environments, with a thorough understanding of de-identification requirements, minimum necessary data access, and audit trail obligations for ML training data.

• A strong track record of creating explainable models in regulated contexts, with the ability to communicate the rationale behind specific model predictions to both technical and clinical audiences.

🏝️ Benefits

• Health care insurance (medical, dental, vision)

• Life Insurance

• Supplemental Insurance

• PTO

• 401K matching

• Sick leave

• Phone/internet reimbursement

• Remote work

• Top of the line machines