[Hiring] Senior Machine Learning Engineer @Revelation Pharma LLC

Role Description

The Senior Machine Learning Engineer is responsible for developing and implementing machine learning solutions that enhance clinical decision-making, patient outcomes, and operational efficiency across Evergreen's telehealth platform. This role combines deep technical expertise with healthcare domain knowledge to design, deploy, and optimize production-ready AI systems that are accurate, explainable, and compliant with regulatory requirements.

Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer will help translate complex clinical and business challenges into scalable machine learning solutions. The ideal candidate is a hands-on engineer who thrives in a fast-paced startup environment, exercises sound technical judgment, and is passionate about building AI capabilities that deliver measurable value while maintaining the highest standards of patient safety and quality.

Note on Scope: The Senior Machine Learning Engineer serves as the technical owner of Evergreen's machine learning and predictive analytics capabilities. This role is responsible for designing, deploying, monitoring, and continuously improving the models that power clinical decision support, patient risk stratification, adherence forecasting, dosage optimization, and agent-driven workflows.

Working closely with Engineering, Clinical Leadership, and Product stakeholders, the Senior Machine Learning Engineer ensures that AI systems are scalable, explainable, compliant, and aligned with patient safety requirements. The role influences machine learning architecture, model governance, and technical standards while serving as a key contributor to Evergreen's long-term AI strategy.

Responsibilities

• Design, training, validation, and monitoring of the core predictive and state-transition models powering the clinical product.
• Production ML pipelines and retraining cadence, including drift detection and rollback.
• Model explainability and validation work needed to support clinical and regulatory review.
• 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.
• Design predictive models that support clinical decision-making while maintaining explainability and regulatory compliance.
• Collaborate with clinical stakeholders to translate protocols, treatment pathways, and pharmacy domain expertise into model features, training labels, and validation criteria.
• Implement and manage the predictive analytics pipeline on AWS, including 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, and operational knowledge sources.
• Contribute to machine learning architecture decisions and establish technical standards that support scalability, reliability, and maintainability.
• Partner with Engineering leadership to evaluate emerging AI technologies and recommend solutions aligned with business objectives and patient safety requirements.
• Own model lifecycle management, including training pipelines, feature stores, model versioning, A/B testing, drift detection, and retraining triggers in production.
• Establish model monitoring and alerting, including prediction quality dashboards, distribution shift detection, and automated alerts when performance degrades below established thresholds.
• Build explainability layers for clinical recommendations to support provider trust, auditability, and regulatory requirements.
• Ensure machine learning systems meet governance, compliance, and documentation standards appropriate for healthcare environments.
• Serve as a senior technical resource for AI and machine learning initiatives across the organization.
• Provide technical guidance and mentorship to engineers contributing to machine learning, AI, and analytics initiatives.
• Partner with Clinical Leadership, Engineering, Product, and Operations teams to ensure models deliver measurable business and patient outcomes.
• Work with DevOps/AgentOps resources to ensure all ML decisions are logged, reproducible, and auditable for regulatory review.

Qualifications

• MS or PhD in a quantitative discipline: Math, Econometrics, Physics, Computational Engineering, or a related field.
• Excellent Python skills, with production experience in a modern ML framework such as PyTorch or TensorFlow.
• Experience developing and deploying production ML models, not just research or notebooks.
• Experience building statistical or neural network models for prediction, classification, or state-transition problems.
• Experience with MLOps: model versioning, automated retraining, drift detection, and production monitoring.

Preferred Qualifications

• Experience developing ML models in a healthcare, clinical, biotech, or pharma context.
• Experience with structured clinical data, including FHIR, claims, or EHR data.
• Experience building explainable models for regulated or safety-sensitive domains.
• Published research, patents, or an advanced thesis involving applied statistical or computational modeling.

Key Performance Metrics

• Model Performance Accuracy: Achievement of target accuracy, precision, recall, and calibration metrics across production models.
• Delivery & Deployment: Successful delivery and deployment of machine learning initiatives against roadmap commitments.
• AI System Uptime & Reliability: Availability and operational performance of machine learning services and inference pipelines.
• Model Reliability: System uptime, performance monitoring, and timely resolution of model drift or degradation.
• Model Drift Detection & Resolution: Timely identification and remediation of model performance degradation.

Requirements

• Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
• Evergreen Telehealth is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.