Senior Staff AI Scientist

GE HealthCare — IND19-01-Bengaluru-EPIP 122 (Phase II)

Job Description Summary The ideal candidate is a PhD-trained AI scientist with 5–8+ years of post-PhD experience driving cutting-edge medical imaging and Physical AI research from conception to real-world clinical deployment. This individual operates as a technical authority and research leader, shaping AI strategy across imaging, embodied and physics-aware AI, foundation models, and multimodal healthcare systems, while mentoring senior scientists and influencing product and clinical roadmaps. They bring deep expertise in learning from real-world data and physical signals, including sensor fusion, spatiotemporal modeling, simulation-to-real transfer, and constraint-aware or physics-informed machine learning as applied to imaging hardware, acquisition pipelines, and clinical workflows. The role requires a strong ability to bridge algorithmic innovation with physical systems, such as scanners, devices, or procedural environments, ensuring robust performance under real-world variability and operational constraints. The candidate possesses a rare combination of deep technical excellence, a strong publication and patent record, and proven experience translating AI research into regulated, commercially deployed healthcare solutions. They have demonstrated success in taking models from research to production, accounting for physical robustness, safety, reliability, and regulatory requirements, and in collaborating closely with clinicians, engineers, and product teams to deliver clinically impactful AI systems. They possess a rare combination of deep technical excellence, strong publication and patent record, and proven experience translating AI research into regulated, commercially deployed healthcare solutions. GE Healthcare is a leading global medical technology and digital solutions innovator. Our mission is to create a world where healthcare has no limits. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a

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