• Build ML models for production use. • Perform in-depth exploratory data analysis to scope ML opportunities, identify potential issues in the data, and lay the groundwork for model design, training & evaluation strategies. • Suggest and implement new features to improve model performance and business logic. • Write and review production-quality PRs daily. • Translate client business problems into ML problems. • Build scalable machine learning models for claim time series forecasting and NLP text understanding. • Continue to grow and learn in the data & ML space with a focus on business problems that require making sense of large-scale structured and unstructured datasets.

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability. • Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions • Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs • Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy • Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability. • Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions • Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs • Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy • Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback

Role Description 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. 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 - Developing the AI analytics chatbot used by all business units This role requires someone who ships ML systems—not someone who just builds notebooks. Qualifications - 3–6 years of hands-on machine learning engineering experience (not just DS notebooks) - Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy - Experience building and tuning LLM and NLP pipelines end-to-end - Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling - Experience deploying ML workloads in Databricks, Azure ML, and Fabric - Strong SQL for feature engineering and model validation - Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred - Strong 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 required Requirements - 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) - 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 - 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 - 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 workflows - Work with data engineering to embed models into CRC applications - Partner with BI analysts to transform model outputs into dashboards - Document methodologies, assumptions, architecture, and validation processes clearly Company Description 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.