Machine Learning Engineer

Job description

A leading healthcare technology organization is seeking a machine learning engineer to build, deploy, and maintain production machine learning systems that support clinical and operational decision-making. This position focuses on turning research-grade models into reliable production services, improving model lifecycle management, and building scalable infrastructure for data-intensive machine learning workflows.

The machine learning engineer will work across model deployment, data pipelines, feature workflows, and platform integration. The role requires close collaboration with researchers, engineers, and clinical stakeholders to support clinical text analysis, predictive modeling, and scalable inference systems in a regulated data environment.

Responsibilities

• Build and maintain production infrastructure for machine learning models used in healthcare and clinical data workflows
• Productionize natural language processing and large language model workflows for clinical text analysis
• Develop and support custom pipelines that extract, classify, and connect clinical entities to defined medical criteria
• Convert research logic into tested, scheduled, observable, and maintainable production pipelines
• Implement monitoring for model performance, data drift, and pipeline reliability
• Manage machine learning lifecycle processes including experiment tracking, model registration, and promotion to production
• Support the design and maintenance of semantic data structures and shared documentation for machine learning and analytics use cases
• Maintain source-to-model data flow visibility across source systems, cloud data platforms, feature datasets, model artifacts, batch inference jobs, and downstream consumers
• Integrate machine learning decision frameworks into existing data and engineering environments
• Participate in sprint-based delivery processes and collaborate across asynchronous planning and development workflows
• Partner with clinical subject matter experts to validate model outputs, reasoning, and annotation quality
• Create technical documentation and engineering guides that improve development standards and dependency management across the team

Required experience and skills

• Bachelor’s degree or higher in computer science, engineering, or a related field
• 3 to 5 years of professional experience as a machine learning engineer, data engineer, or in a similar role building scalable machine learning and data applications
• Strong programming skills in Python
• Hands-on experience with Databricks in a cloud environment
• Experience designing and operating clusters and job orchestration for scalable data and machine learning workloads
• Experience building, maintaining, and deploying automated CI and CD pipelines for data and machine learning workflows
• Strong SQL skills with experience working at scale, including table dependency management and query optimization
• Experience making technical decisions that balance immediate delivery needs with long-term platform value
• Experience deploying machine learning models into production and managing end-to-end MLOps workflows
• Understanding of machine learning methods, statistical techniques, and production model management
• Experience designing and optimizing data pipelines, ETL processes, and data ingestion workflows
• Expertise in configuring and tuning Databricks clusters and compute resources for large-scale processing
• Familiarity with Docker and Kubernetes
• Experience with CI and CD tools such as GitHub Actions or GitLab CI
• Experience monitoring and optimizing distributed data systems and machine learning infrastructure, including Apache Spark
• Experience working with sensitive data and compliance-focused environments such as HIPAA-regulated systems