Senior Staff AI Scientist

Overview

Senior Staff AI Scientist leading research and technical strategy for medical image analysis, Physical AI-enabled imaging systems, foundation models, and multimodal healthcare AI, from research through clinical deployment.

What you'll do

• 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 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, and physics-informed ML, constraint-aware learning, simulation-to-real transfer, and embodied perception.
• Establish technical standards for experimental rigor, physical robustness, uncertainty quantification, clinical validation, fairness, and generalization across imaging devices, sites, and populations.
• Partner with clinicians, radiologists, pathologists, imaging scientists, and regulatory stakeholders to formulate AI problem statements grounded in clinical workflows and the 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, and 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 in regulated healthcare environments.
• Act as 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.
• 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.

What you'll need

• 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.
• 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 end-to-end ownership: research ideation, system design, validation, and 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 deployability.
• Recognized as a trusted technical advisor by engineering, clinical, imaging, and product leadership.
• 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; and 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.

Details

• Location: Bengaluru, India (EPIP 122, Phase II).
• Relocation assistance provided.