Foundation AI Research Scientist

Siemens Healthineers

6d•$128,700 - $176,957•Remote

About The Position

Siemens Healthineers is seeking a Foundation AI Research Scientist to advance the development of next-generation multimodal foundation models for healthcare. In this role, you will research, design, and develop large-scale AI models capable of learning and reasoning across complex medical data, including medical imaging, clinical text, electronic health records, real-time monitoring data, and other healthcare information. You will explore state-of-the-art approaches in multimodal foundation models, vision-language learning, model distillation, few- and zero-shot learning, and next-generation AI architectures to dramatically accelerate the development of new clinical AI capabilities and enable systems that can scale across hundreds of findings and use cases. Working at the intersection of foundational AI research and clinical application, you will collaborate closely with AI scientists, software engineers, and clinical experts to translate breakthrough research into scalable technologies that can ultimately support clinical workflows worldwide.

Requirements

PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a related technical field with research focused on machine learning, artificial intelligence, computer vision, medical imaging, or a closely related discipline.
Demonstrated research experience developing advanced machine learning or deep learning models.
Strong understanding of modern deep learning architectures and training methodologies.
Experience with one or more areas such as: Foundation models and large-scale pretrained models, Multimodal learning, Vision-language models, Computer vision, Generative AI, Self-supervised or representation learning, Few-shot or zero-shot learning, Knowledge/model distillation, Medical imaging AI
Strong programming skills and experience using modern deep learning frameworks.
Ability to translate emerging research concepts into practical AI systems and experimental prototypes.
Strong scientific communication and cross-functional collaboration skills.
Exceptional candidates with a Master’s degree and significant research or industry experience developing advanced AI systems will also be considered.

Nice To Haves

Research experience with large-scale multimodal or vision-language foundation models.
Experience pretraining, fine-tuning, adapting, or evaluating large AI models.
Experience developing AI models using medical imaging or other healthcare data.
Experience with model compression, knowledge distillation, parameter-efficient adaptation, or related techniques.
Experience with self-supervised learning, weakly supervised learning, few-shot learning, or zero-shot generalization.
Experience evaluating model robustness, uncertainty, interpretability, or safety.
Strong publication record at leading AI, computer vision, or medical imaging conferences and journals.
Experience translating research into production or clinically relevant AI systems.
Track record of innovation demonstrated through publications, patents, open-source contributions, or deployed AI technologies.

Responsibilities

Designing and developing large-scale multimodal foundation models for healthcare and medical imaging applications.
Researching next-generation AI architectures and learning strategies capable of delivering significant improvements in downstream clinical performance.
Developing innovative model distillation, transfer learning, few-shot, and zero-shot learning approaches to accelerate expansion of AI capabilities across a broad range of clinical findings and use cases.
Advancing multimodal AI capable of learning across medical images, clinical text, patient history, and other healthcare data sources.
Exploring approaches that move AI systems toward increasingly autonomous capabilities supporting clinical decision-making, workflow automation, and personalized patient care.
Designing methods to improve model generalization, robustness, uncertainty awareness, interpretability, and reliability in complex clinical environments.
Staying at the forefront of developments in foundation models, multimodal learning, computer vision, generative AI, and medical AI research.
Collaborating closely with AI researchers, software engineers, clinical experts, and product teams to connect foundational AI research with real-world healthcare applications.
Supporting the integration of multimodal AI systems incorporating medical imaging, electronic health records, clinical text, real-time monitoring, and other healthcare data.
Translating research prototypes and novel AI methodologies into scalable approaches suitable for real-world clinical environments.
Helping enable the transition of advanced AI technologies from research into solutions capable of deployment across healthcare institutions worldwide.
Advancing research in AI safety, reliability, robustness, and interpretability for clinical AI systems.
Developing approaches that help ensure foundation models perform reliably across diverse patient populations, clinical environments, and data distributions.
Supporting the development of AI technologies aligned with clinical-grade performance expectations, responsible AI principles, and applicable healthcare requirements.
Contributing technical expertise toward evolving healthcare AI regulations and emerging AI safety frameworks.
Publishing impactful research in leading journals and conferences such as TPAMI, Medical Image Analysis (MedIA), IEEE Transactions on Medical Imaging (TMI), CVPR, ICCV, ECCV, and other relevant venues.
Identifying opportunities for novel intellectual property and contributing to patent development.
Collaborating with internal and external research communities to advance the state of the art in foundation AI for healthcare.