Top Healthtech Machine Learning Engineer Jobs

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

Lead end-to-end data science projects: define problems, build and deploy production-grade ML/DL and optimization solutions, design data pipelines on Databricks/Azure Synapse, create dashboards (Power BI/Tableau/Databricks), implement MLOps/CI-CD and XAI, monitor production models, and mentor junior staff while translating insights for stakeholders.

Lead cutting-edge machine learning research, develop algorithms, and deploy production-ready models in a healthcare-focused AI environment. Collaborate with engineers and clinicians to optimize healthcare systems.

Design, build, and deploy production-grade AI/ML models and LLM-powered applications; develop ETL and feature pipelines; implement RPA and orchestration workflows; integrate models via APIs and containers; ensure HIPAA-compliant monitoring, drift detection, and reliability; collaborate cross-functionally to apply AI to healthcare workflows and optimize automation ROI.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Lead design, delivery, and scaling of AI-driven forecasting products (demand, inventory, GTN). Own product roadmap, model lifecycle, MLOps, governance, and explainability. Partner with Finance, Commercial, Supply, and BI&T to embed forecasts into portfolio planning, scenario analysis, and launch readiness. Build cross-functional teams, manage vendors, and drive adoption, monitoring, and continuous improvement.

The Principal Machine Learning Engineer will lead hands-on model development, oversee predictive modeling, and drive AI initiatives, ensuring models are effective and operationally sound.

The R&D Engineer Intern will design machine learning pipelines, translate research into products, and develop microservices for biomedical engineering applications.

The Applied Scientist will develop and enhance machine learning models, design A/B tests, evaluate performance, and collaborate with stakeholders to enhance decision-making and operational workflows in healthcare.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.