Note: The job is a remote job and is open to candidates in USA. Curate Partners is seeking a highly skilled Machine Learning Engineer to join their healthcare client's AIRML team. This role involves building and maintaining machine learning models that impact patient care, while collaborating with AI researchers and managing the full machine learning lifecycle.
Responsibilities
- Clinical 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
- MLOps: 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)
- Agile 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
Skills
- BS/BTech (or higher) in Computer Science, Engineering or a related field
- 3-5 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 Python
- Hands-on experience with Databricks cloud infrastructure
- Experience with clusters and jobs orchestration, specifically designing scalable compute orchestration to bypass processing bottlenecks
- Experience building, maintaining, and deploying automated CI/CD pipelines for data and machine learning workflows
- Hands-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 value
- 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 techniques
- Experience in designing, building, and optimizing data pipelines, ETL processes, and data ingestion systems
- Expertise in configuring, tuning, and managing Databricks clusters and compute resources for large-scale data processing
- Knowledge of containerization and orchestration technologies such as Docker and Kubernetes
- Familiarity 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)
- Experience with health-tech systems, like Electronic Health Records, Clinical data, etc
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