Machine Learning Engineer - Comprehensive Rehab Consultants

About Comprehensive Rehab Consultants (CRC)

Comprehensive Rehab Consultants partners with skilled nursing facilities to improve clinical quality, efficiency, and financial performance. CRC is recognized as an innovator in the post-acute care space, developing new AI-driven care and revenue models that decrease hospitalizations and improve outcomes.

We are seeking a Machine Learning Engineer to drive the next generation of CRC’s AI platform. This role is engineering-heavy, focused on building, tuning, and deploying scalable ML/NLP/LLM systems that power our clinician tools, financial analytics, contract modeling, DFS/PDPM engines, readmission risk scores, and enterprise analytics chatbot.

Role Summary

You will design, optimize, and productionize machine learning systems using CRC’s full internal data environment. This includes tuning an existing NLP engine, developing statistical/ML models (including ordered probit and regression-based DFS calculator), building internal LLMs trained on CRC documents, and developing the AI analytics chatbot used by all business units.

This role requires someone who ships ML systems—not someone who just builds notebooks.

Key Responsibilities

Machine Learning & Statistical Modeling

• Build, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecasting
• Engineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)

NLP & LLM Engineering

• Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring
• Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models
• Develop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelines
• Build automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QA

AI-Driven Product Development

• Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources
• Integrate ML models into production services using notebooks, APIs, or batch inference jobs
• Support creation of AI-generated reporting, insights summaries, and automated clinical/financial narratives

MLOps & Engineering

• Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD
• Implement model monitoring, drift detection, and automated retraining
• Package and deploy reproducible models via APIs or scheduled Fabric/Databricks