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.This role requires someone who ships ML systems—not someone who just builds notebooks.Role SummaryYou 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.Key ResponsibilitiesMachine Learning & Statistical ModelingBuild, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecastingEngineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)NLP & LLM EngineeringTune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoringBuild custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector modelsDevelop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelinesBuild automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QAAI-Driven Product DevelopmentArchitect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sourcesIntegrate ML models into production services using notebooks, APIs, or batch inference jobsSupport creation of AI-generated reporting, insights summaries, and automated clinical/financial narrativesMLOps & EngineeringBuild maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CDImplement model monitoring, drift detection, and automated retrainingPackage and deploy reproducible models via APIs or scheduled Fabric/Databricks workflowsCollaboration & DeliveryWork with data engineering to embed models into CRC applicationsPartner with BI analysts to transform model outputs into dashboardsDocument methodologies, assumptions, architecture, and validation processes clearlyRequired Skills & Experience3–6 years of hands-on machine learning engineering experience (not just DS notebooks)Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCyExperience building and tuning LLM and NLP pipelines end-to-endExperience with regression, ordered probit/logit, hierarchical models, and general statistical modelingExperience deploying ML workloads in Databricks, Azure ML, and FabricStrong SQL for feature engineering and model validationPrior experience working with healthcare data (EMR, claims, RCM, CMS) preferredStrong communication and the ability to explain complex ML systems to non-technical stakeholders.Proactive, self-managing engineer who can independently own ML systems end-to-end.Fluent English requiredPreferred QualificationsExperience with:Retrieval-Augmented Generation (RAG) pipelinesVector databases (FAISS, Chroma, Pinecone, Qdrant)Enterprise chatbot frameworksMLflow, CI/CD, GitHub Actions, and model versioningPower BI integration for ML outputsFHIR/SMART on FHIRCertifications considered:Databricks ML Associate/ProfessionalAzure AI Engineer AssociateDeepLearning.AI NLP/LLM specializations
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