Machine Learning Engineer

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Revelation Pharma

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2020

💊 Pharmaceuticals

⚕️ Healthcare Insurance

🤝 B2B

Pharmaceuticals • Healthcare Insurance • B2B

Revelation Pharma is a national network of 503A and 503B compounding pharmacies dedicated to providing innovative and quality pharmacy preparations and services. They aim to lead the way in the compounding pharmacy industry, focusing on patient-centric care and exploring opportunities for growth and collaboration among pharmacies. Their mission includes a commitment to enhancing the pharmacy business and ensuring a seamless transition for those looking to sell or partner with reputable compounding pharmacies.

📋 Description

• 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 production • Build explainability layers for clinical recommendations • Collaborate with the clinical team to translate clinical protocols and pharmacy domain knowledge into model features, training labels, and validation criteria • Establish model monitoring and alerting – prediction quality dashboards, distribution shift detection, and automated alerts when model performance degrades below clinical safety thresholds • Work with the DevOps/AgentOps teammate to ensure all ML decisions are logged, reproducible, and auditable for regulatory review

🎯 Requirements

• 6+ years in ML engineering or applied data science, with at least 3 years shipping ML models to production in a healthcare, biotech, or clinical domain • Direct experience building clinical decision support, risk stratification, or patient outcome prediction models – must understand the regulatory and ethical implications of ML in healthcare • Production experience with LLM-based agent systems – must have built or significantly contributed to a system where an LLM makes consequential decisions with safety guardrails • Demonstrated ability to collaborate with clinical domain experts (physicians, pharmacists, clinical researchers) to translate domain knowledge into model design decisions • Experience working in HIPAA-regulated environments – must understand de-identification requirements, minimum necessary data access, and audit trail requirements for ML training data • Track record of building explainable models in regulated contexts – must be able to articulate why a model makes a specific prediction 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