
Senior Software Engineer - Machine Learning & Artificial Intelligence (ML & AI)
- King of Prussia, PA
- Permanent
- Full-time
- Design, train, and deploy predictive analytics models for disease detection, patient risk assessment, and personalized treatment recommendations.
- Develop computer vision algorithms for medical imaging analysis, assisting in diagnostics and anomaly detection.
- Implement natural language processing (NLP) models for extracting insights from clinical notes, electronic health records (EHRs), and medical literature.
- Architect and develop scalable AI-driven applications that integrate with healthcare systems.
- Ensure data governance, compliance (HIPAA, GDPR), and security best practices.
- Optimize AI models for real-time processing and efficient deployment in cloud or on-premise environments.
- Design and maintain data pipelines for structured and unstructured healthcare data.
- Ensure data quality, preprocessing, and feature engineering for AI model training.
- Work with big data technologies to manage large-scale healthcare datasets.
- Ensure AI solutions comply with HIPAA, GDPR, and other healthcare regulations.
- Implement robust security measures to protect patient data and prevent unauthorized access.
- Develop explainable AI (XAI) models to enhance transparency and trust in AI-driven healthcare decisions.
- Work closely with clinicians, researchers, and administrators to define AI-driven solutions that improve patient outcomes.
- Communicate AI insights effectively to non-technical stakeholders, ensuring alignment with healthcare objectives.
- Establish monitoring tools to track AI model performance and reliability.
- Provide technical support for AI-driven healthcare applications, troubleshooting issues and optimizing performance.
- Implement model retraining strategies to adapt AI solutions to evolving healthcare data and requirements.
- Ensure long-term sustainability of AI models through version control, documentation, and stakeholder feedback.
- Provide technical leadership, mentoring junior engineers and guiding AI strategy within the organization.
- Bachelor's or Master's degree in Computer Science, AI, or a related field.
- 5+ years of experience in ML and AI development, with a strong focus on healthcare interoperability and platform architecture.
- Machine Learning & AI Expertise:
- Strong proficiency in Python, TensorFlow, PyTorch, and other ML frameworks.
- Experience with natural language processing (NLP) and computer vision for healthcare applications.
- Knowledge of reinforcement learning and large language models (LLMs).
- Familiarity with AI observability and experimenting tools such as MLFlow.
- Platform Architecture & Scalability:
- Expertise in microservices architecture, ensuring modular and scalable AI solutions.
- Proficiency in containerization (Docker, Kubernetes) for efficient deployment.
- Experience with cloud platforms (AWS, Azure, GCP) for scalable AI integration.
- Strong understanding of high-performance computing for large-scale healthcare data processing.
- Ability to design interoperable systems that integrate with healthcare standards like FHIR, HL7, and EHR systems.
- Ability to build ML inference endpoints for model serving.
- Data Engineering & Security:
- Build and maintain data pipelines for structured and unstructured healthcare data.
- Ensure data governance, compliance (HIPAA, GDPR), and security best practices.
- Work with big data technologies to manage large-scale healthcare datasets.
- Experience working with data warehouse solutions such as Snowflake, Data Lake, or Oracle ADB.
- Software Engineering & System Integration:
- Develop modular, scalable AI-driven applications for healthcare analytics.
- Optimize AI models for real-time processing and efficient deployment.
- Implement CI/CD pipelines for continuous integration and deployment.
- Leadership & Collaboration:
- Provide technical leadership, mentoring junior engineers and guiding AI strategy.
- Work closely with clinicians, researchers, and administrators to define AI-driven solutions.
- Communicate AI insights effectively to non-technical stakeholders.
- Machine Learning Model Development: Design, build, and deploy ML models for healthcare applications, such as predictive analytics, medical imaging analysis, and patient risk assessment.
- Software Engineering: Develop scalable and modular software architectures that integrate ML models into healthcare systems.
- Cloud Infrastructure: Develop, deploy, and scale ML models, ensuring optimal performance and cost-effectiveness.
- ML Testing: Design and implement comprehensive test suites to assess the performance of deployed machine learning models, pinpointing areas for enhancement and working closely with data scientists to optimize model accuracy.
- Data Engineering: Work with structured and unstructured healthcare data, ensuring seamless data integration and pipeline efficiency.
- Compliance & Security: Ensure AI solutions adhere to healthcare regulations (HIPAA, GDPR) and maintain high security standards.
- Collaboration: Work with clinicians, researchers, and administrators to define AI-driven solutions that improve patient outcomes.
- Monitoring & Optimization: Implement tools for active monitoring of ML models to ensure performance and reliability.
- Challenging and rewarding work environment
- Growth and development opportunities within UHS and its subsidiaries
- Competitive Compensation
- Excellent Medical, Dental, Vision and Prescription Drug Plan
- 401k plan with company match
- Generous Paid Time Off