{"schemaVersion":"jobsearcher.job.v1","id":"b50aeb5ab9a7ff4f61f5ff84","url":"https://jobsearcher.com/jobs/b50aeb5ab9a7ff4f61f5ff84","canonicalUrl":"https://jobsearcher.com/jobs/b50aeb5ab9a7ff4f61f5ff84","title":"Machine Learning Engineer","description":"Role OverviewOur healthcare client is seeking a highly skilled Machine Learning Engineer to join the AIRML (AI Research & Machine Learning) team. In this role, you will be responsible for building the infrastructure for, productionizing, and maintaining state-of-the-art machine learning models that directly impact patient care and healthcare outcomes. You will work across risk and non-risk workstreams, focusing on clinical note analysis infrastructure, risk modeling productionization, and scalable deployment systems. This role serves as a key engineering partner to AI Researchers, productionizing research logic, maintaining model and feature pipelines, debugging data/model issues, and enabling reliable iteration across research and production workflows.(Please note this role is only open to U.S citizens and Green Card holders)Key ResponsibilitiesModel Development & ProductionizationClinical NLP: Productionize Large Language Model (LLM) tagging of clinical notes to enhance HCC risk modeling accuracy.Custom Pipelines: Develop and maintain in-house entity recognition pipelines that link clinical entities to specific medical criteria.Model Stability: Migrate complex research logic into maintainable, tested, scheduled, and observable production pipelines to ensure long-term model reliability and stability. Setup data drift monitoring.Engineering ExcellenceMLOps: Manage the full machine learning lifecycle, including model registration, tracking experiments via MLflow, and automating production promotion.Data Strategy: Collaborate on the development of a local semantic data layer to improve accessibility and maintain unified documentation for risk research.Data Flow & Lineage: Understand and maintain source-to-model data flows across source systems, Snowflake, Databricks, feature datasets, MLflow artifacts, batch inference jobs, and downstream consumers.System Integration: Integrate decision-making frameworks into existing engineering pipelines (e.g., Databricks and Snowflake environments).Collaboration & Agile DeliveryAgile Processes: Participate in 3-week sprint cycles and engage in asynchronous planning sessions to align with broader engineering timelines.Stakeholder Engagement: Coordinate with clinical experts to validate model reasoning chains and resolve conflicting feedback during annotation cycles.Mentorship & Growth: Contribute to the team's collective knowledge by creating development guides for dependency management and best practices.Minimum QualificationsBS/BTech (or higher) in Computer Science, Engineering or a related field3-5 years of professional, post-bachelor’s experience as a Machine Learning Engineer, Data Engineer, or similar role building scalable ML and data applications as part of a cross-functional team (internships during bachelor's studies do not count toward this requirement).Strong programming proficiency in PythonHands-on experience with Databricks cloud infrastructureExperience with clusters and jobs orchestration, specifically designing scalable compute orchestration to bypass processing bottlenecksExperience building, maintaining, and deploying automated CI/CD pipelines for data and machine learning workflowsHands-on experience large-scale SQL (e.g. Snowflake), table dependencies, and query optimization.Experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business valuePreferred KSA (Knowledge, Skills, and Abilities)Experience with health-tech systems, like Electronic Health Records, Clinical data, etc.Domain Specific ExperienceMachine Learning & Data Infrastructure:Experience deploying machine learning models into production environments and managing the end-to-end model lifecycle (MLOps)Proficiency in machine learning algorithms, techniques, and statistical data techniquesExperience in designing, building, and optimizing data pipelines, ETL processes, and data ingestion systemsExpertise in configuring, tuning, and managing Databricks clusters and compute resources for large-scale data processingKnowledge of containerization and orchestration technologies such as Docker and KubernetesFamiliarity with continuous integration and continuous deployment (CI/CD) pipelines using modern tools (e.g., GitHub Actions, GitLab CI)Experience in performance monitoring and optimization of data systems, ML infrastructure, and distributed computing frameworks (e.g., Apache Spark)Experience with security and systems that handle sensitive data (e.g., HIPAA compliance)About the TeamThe Risk AI Research Team is a specialized AI research group focused on developing machine learning models supported through LLM verification and clinical decision support systems for risk adjustment, HCC verification, and care gap identification. The mission of the team is to advance healthcare quality and risk adjustment accuracy through AI/ML solutions for clinical verification, hierarchical condition category (HCC) prediction, and physician-assisted annotation systems.","company":"Curate Partners","rawCompany":"curate partners","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-14T13:26:53.324Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"Role OverviewOur healthcare client is seeking a highly skilled Machine Learning Engineer to join the AIRML (AI Research & Machine Learning) team. In this role, you will be responsible for building the infrastructure for, productionizing, and maintaining state-of-the-art machine learning models that directly impact patient care and healthcare outcomes. You will work across risk and non-risk workstreams, focusing on clinical note analysis infrastructure, risk modeling productionization, and scalable deployment systems. This role serves as a key engineering partner to AI Researchers, productionizing research logic, maintaining model and feature pipelines, debugging data/model issues, and enabling reliable iteration across research and production workflows.(Please note this role is only open to U.S citizens and Green Card holders)Key ResponsibilitiesModel Development & ProductionizationClinical NLP: Productionize Large Language Model (LLM) tagging of clinical notes to enhance HCC risk modeling accuracy.Custom Pipelines: Develop and maintain in-house entity recognition pipelines that link clinical entities to specific medical criteria.Model Stability: Migrate complex research logic into maintainable, tested, scheduled, and observable production pipelines to ensure long-term model reliability and stability. Setup data drift monitoring.Engineering ExcellenceMLOps: Manage the full machine learning lifecycle, including model registration, tracking experiments via MLflow, and automating production promotion.Data Strategy: Collaborate on the development of a local semantic data layer to improve accessibility and maintain unified documentation for risk research.Data Flow & Lineage: Understand and maintain source-to-model data flows across source systems, Snowflake, Databricks, feature datasets, MLflow artifacts, batch inference jobs, and downstream consumers.System Integration: Integrate decision-making frameworks into existing engineering pipelines (e.g., Databricks and Snowflake environments).Collaboration & Agile DeliveryAgile Processes: Participate in 3-week sprint cycles and engage in asynchronous planning sessions to align with broader engineering timelines.Stakeholder Engagement: Coordinate with clinical experts to validate model reasoning chains and resolve conflicting feedback during annotation cycles.Mentorship & Growth: Contribute to the team's collective knowledge by creating development guides for dependency management and best practices.Minimum QualificationsBS/BTech (or higher) in Computer Science, Engineering or a related field3-5 years of professional, post-bachelor’s experience as a Machine Learning Engineer, Data Engineer, or similar role building scalable ML and data applications as part of a cross-functional team (internships during bachelor's studies do not count toward this requirement).Strong programming proficiency in PythonHands-on experience with Databricks cloud infrastructureExperience with clusters and jobs orchestration, specifically designing scalable compute orchestration to bypass processing bottlenecksExperience building, maintaining, and deploying automated CI/CD pipelines for data and machine learning workflowsHands-on experience large-scale SQL (e.g. Snowflake), table dependencies, and query optimization.Experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business valuePreferred KSA (Knowledge, Skills, and Abilities)Experience with health-tech systems, like Electronic Health Records, Clinical data, etc.Domain Specific ExperienceMachine Learning & Data Infrastructure:Experience deploying machine learning models into production environments and managing the end-to-end model lifecycle (MLOps)Proficiency in machine learning algorithms, techniques, and statistical data techniquesExperience in designing, building, and optimizing data pipelines, ETL processes, and data ingestion systemsExpertise in configuring, tuning, and managing Databricks clusters and compute resources for large-scale data processingKnowledge of containerization and orchestration technologies such as Docker and KubernetesFamiliarity with continuous integration and continuous deployment (CI/CD) pipelines using modern tools (e.g., GitHub Actions, GitLab CI)Experience in performance monitoring and optimization of data systems, ML infrastructure, and distributed computing frameworks (e.g., Apache Spark)Experience with security and systems that handle sensitive data (e.g., HIPAA compliance)About the TeamThe Risk AI Research Team is a specialized AI research group focused on developing machine learning models supported through LLM verification and clinical decision support systems for risk adjustment, HCC verification, and care gap identification. The mission of the team is to advance healthcare quality and risk adjustment accuracy through AI/ML solutions for clinical verification, hierarchical condition category (HCC) prediction, and physician-assisted annotation systems.","datePosted":"2026-08-14T13:26:53.324Z","dateModified":"2026-08-14T13:26:53.324Z","hiringOrganization":{"@type":"Organization","name":"Curate Partners","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b50aeb5ab9a7ff4f61f5ff84"},"url":"https://jobsearcher.com/jobs/b50aeb5ab9a7ff4f61f5ff84"}}