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Senior Machine Learning Engineer

Revelation Pharma LLC
8 hours ago
Full-time
Remote
Worldwide
ML & AI Engineering


EVERGREEN TELEHEALTH

SVP OF OPERATIONS

ROLE SUMMARY

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

Clinical Intelligence & Predictive Modeling

  • 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

AI Architecture & Platform Development

  • 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

Model Lifecycle & Governance

  • 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

Cross-Functional Leadership

  • 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

 

REQUIRED TECHNICAL SKILLS

  • Applied Machine Learning (Expert): Proven experience building, training, and deploying production ML models (classification, regression, and time-series forecasting). Strong foundation in statistical modeling, experimental design, and A/B testing. 
  • Production ML / MLOps (Expert): Hands-on experience deploying and maintaining ML systems in production, including model versioning, monitoring, drift detection, retraining pipelines, and CI/CD for ML workflows. 
  • Healthcare ML / High-Stakes Modeling (Strong): Experience building models in regulated or high-stakes environments (healthcare, biotech, clinical, or financial systems), including safety, explainability, and validation requirements. 
  • LLM & NLP Systems (Strong): Experience with LLM-based applications including RAG architecture, embeddings, prompt engineering, evaluation frameworks, and hallucination mitigation in production systems. 
  • AWS ML Stack (Strong): Experience with key AWS services such as SageMaker, Bedrock, Amazon Forecast, and S3-based data pipelines. Ability to design and operate end-to-end ML systems in AWS. 
  • Data & Feature Engineering (Strong): Experience building scalable feature pipelines and working with complex datasets, ideally including healthcare or structured medical data (FHIR or equivalent). Strong SQL and data modeling skills. 
  • Model Evaluation & Safety Design (Strong): Ability to design evaluation frameworks for ML systems where accuracy alone is insufficient—must include calibration, confidence thresholds, and safe failure modes.

QUALIFICATIONS & EDUCATION REQUIREMENTS

  • 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


Metric

Description

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



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.