Senior Staff AI Scientist

GE HealthCare · India

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 difference, and every difference builds a healthier world.

Job Description

Roles & Responsibilities:

Research & Technical Leadership:

Define and own the long-term research vision for medical image analysis, Physical AI–enabled imaging systems, foundation models, and multimodal healthcare AI across clinical domains.
Architect and lead the development of state-of-the-art detection, segmentation, classification, reconstruction, and quantitative imaging systems, incorporating physics-aware modeling, spatiotemporal reasoning, and sensor-informed learning, deployed in real clinical workflows.
Drive innovation across:
- Vision and multimodal transformers
- Large-scale foundation models
- Self-supervised, weakly supervised, and semi-supervised learning
- Physics-informed ML, constraint-aware learning, simulation-to-real transfer, and embodied perception paradigms relevant to medical imaging and procedural environments.
Establish technical standards for experimental rigor, physical robustness, uncertainty quantification, clinical validation, fairness, and generalization across imaging devices, sites, and populations.

Clinical Translation & Real-World Impact:

Partner deeply with clinicians, radiologists, pathologists, imaging scientists, and regulatory stakeholders to formulate AI problem statements grounded in clinical workflows and physical realities of imaging systems.
Lead dataset and system design strategies including:
- Multi-site and multi-device data curation
- Annotation frameworks aligned with clinical and procedural context
- Bias mitigation, domain shift analysis, and hardware- and protocol-aware generalization assessment.
Guide AI solutions through clinical validation, regulatory pathways, and real-world deployment, ensuring safety, robustness to physical variability, and compliance with regulated healthcare environments.

Organizational & Cross-Functional Leadership:

Act as a technical mentor and design reviewer for senior scientists, postdocs, and junior researchers across AI, imaging, and Physical AI domains.
Influence product direction, portfolio prioritization, and platform strategy in collaboration with engineering, imaging hardware teams, MLOps, product, and clinical partners.
Serve as a go-to expert for medical imaging AI and Physical AI systems, advising on architecture decisions, risk trade-offs, and long-term strategy.
Represent the organization externally through publications, patents, invited talks, and collaborations with academia, healthcare systems, and industry partners.

Research Excellence & Thought Leadership:

Consistently publish in top-tier venues (e.g., MICCAI, CVPR, NeurIPS, TMI, MedIA, Radiology AI, Nature family journals), including work that bridges learning-based AI with physical modeling and real-world systems.
Drive IP creation through high-value patents covering algorithmic innovation, system-level design, and physics-aware or multimodal AI methods.
Stay ahead of emerging trends in Physical AI, foundation models, multimodal and embodied AI, federated learning, regulatory science, and AI safety in healthcare.

Technical Expertise (Senior-Level Expectations):

Deep mastery of deep learning frameworks (PyTorch preferred) and large-scale training, fine-tuning, optimization, and deployment.
Advanced expertise in medical image analysis across 2D/3D/4D imaging and multimodal MRI/CT/PET/X-ray/Ultrasound, using MONAI, SimpleITK, OpenCV, and related tools.
Proven experience with:
- Vision and multimodal foundation models
- Self-, weakly-, and semi-supervised learning at scale
- Domain adaptation and generalization across institutions and devices
- Physics-informed ML, simulation-based learning, spatiotemporal modeling, sensor fusion, and uncertainty-aware inference
Strong understanding of MLOps, experiment tracking, reproducibility, scalable research platforms, and model lifecycle management.
Solid grasp of data governance, model risk management, privacy-preserving ML, regulatory expectations (FDA, CE, SaMD), and real-world safety considerations for AI operating on physical systems.

Educational Qualifications:

PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Applied Physics, Robotics, or a closely related field.
5–8+ years of post-PhD experience in medical imaging AI, healthcare AI, Physical AI, or applied AI research.

Required Skills:

Has led large, system-level research initiatives rather than isolated models, often spanning algorithms, data, and physical constraints.
Strong publication and/or patent track record in top AI, imaging, or systems-oriented venues.
Demonstrated history of end-to-end ownership: research ideation → system design → validation → regulated, real-world deployment.
Experience operating in matrixed, cross-functional, and clinically driven environments involving both software and physical systems.
Comfortable making architecture and strategy decisions under ambiguity and real-world constraints.
Balances scientific rigor with pragmatism, optimizing for clinical impact, robustness, and deploy ability.
Recognized as a trusted technical advisor by engineering, clinical, imaging, and product leadership.

Inclusion and Diversity:

GE Healthcare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.

We expect all employees to live and breathe our behaviours: to act with humility and build trust; lead with transparency; deliver with focus, and drive ownership – always with unyielding integrity.

Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything you’d expect from an organization with global strength and scale, and you’ll be surrounded by career opportunities in a culture that fosters care, collaboration, and support.

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Additional Information

Relocation Assistance Provided: Yes

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