AI ML Engineer

Job Description

Job Description SummaryAs an AI ML Engineer in GE HealthCare, you will design, develop, and deploy machine learning models and supporting software for automated measurements, detection and classification of anatomy in ultrasound imaging systems. This role spans data strategy, model architecture, integration with on-scanner services, and performance validation to meet clinical, regulatory, and usability requirements. You will be part of General Imaging Primary Care Ultrasound engineering team defining, developing, and AI Features for Medical Ultrasound products in a fast-paced, agile development environment, utilizing the latest software development technologies and infrastructure.

We are an industry leader in medical ultrasound in the market, and you will be contributing to cutting-edge innovations that shape the future of healthcare. Job DescriptionRoles and ResponsibilitiesSoftware Design & Implementation: Design and implement AI Features for GE Healthcare’s Medical Ultrasound products, ensuring adherence to high standards of quality and performance. Requirements & Specifications: Author software requirements and design specifications, acting as a feature lead by managing scheduling, estimating efforts, and overseeing implementation. Testing & Verification: Develop and execute unit, integration, and system tests to validate design and implementation throughout development cycles.

Data Strategy & Acquisition: Define data collection plans aligned with regulatory and performance requirements. Annotation: Develop annotation protocols for segmentation and caliper placement based on anatomical and workflow requirements working with Clinical partnersDataset Curation: Define image stratification categories, pathology/anatomy types, confounders and probe/system metadata to ensure AI model performance across variety.

Organize, maintain and summarize structured datasets for training, validation, and testing. Model Development: Collaborate with AI Scientists on model architecture decisions by providing key insights. Generate augmentations to address low-data regime.

Validate data readiness for SSL pretraining and multi-task learning pipelines. Integration & Software Engineering Integrate AI models into the ultrasound platform. Implement pre- and post-processing pipelines for segmentation and measurement inferencing.

Ensure seamless integration with existing UI/UX workflows. Performance Evaluation: Design evaluation strategies for model accuracy, segmentation quality, and measurement error metrics.

Conduct subgroup analyses and report compliance with CTQs. Lead validation studies for cross-system performance. Risk & Compliance: Identify technical/Clinical risks and propose mitigation plans.

Ensure compliance with quality, privacy, security, and regulatory standards during development. Agile Collaboration: Collaborate with project team members using the Agile Scrum methodology to deliver high-quality software solutions. Mentorship: Mentor and guide other engineers on the team, promoting the development of high-quality software using static analysis tools, design reviews, and code reviews.

Technical Leadership: Lead by example, driving engineering best practices to initiate, plan, and execute large-scale, cross-functional, and company-wide critical programs. Analyze design and develop a roadmap and implementation plan based upon a current vs. future state in a cohesive architecture viewpoint.

Continuous Improvement: Support and drive the team’s efforts in continuous improvement by enhancing efficiency, eliminating duplication, and leveraging product and technology reuse. Code Standards & Performance: Write code that meets established standards and delivers the desired functionality. Understand and assess application performance to ensure optimal outcomes.

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