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Varian

Tech-enabled radiation oncology hardware and software

Foundation AI Research Scientist

AI ResearchConfirmed live in the last 24 hours
Full-timeUnited States$128.7k - $177kHybrid

About the Job

Requirements

  • A 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; exceptional candidates with a Master's degree and significant research or industry experience developing advanced AI systems will also be considered.
  • Demonstrated research experience developing advanced machine learning or deep learning models.
  • Strong understanding of modern deep learning architectures and training methodologies.
  • Experience in 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 or model distillation, or 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.

Responsibilities

  • Design and develop large-scale multimodal foundation models for healthcare and medical imaging applications.
  • Research next-generation AI architectures and learning strategies capable of delivering significant improvements in downstream clinical performance.
  • Develop 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.
  • Advance multimodal AI capable of learning across medical images, clinical text, patient history, and other healthcare data sources.
  • Explore approaches that move AI systems toward increasingly autonomous capabilities supporting clinical decision-making, workflow automation, and personalized patient care.
  • Design methods to improve model generalization, robustness, uncertainty awareness, interpretability, and reliability in complex clinical environments.
  • Stay at the forefront of developments in foundation models, multimodal learning, computer vision, generative AI, and medical AI research.
  • Collaborate closely with AI researchers, software engineers, clinical experts, and product teams to connect foundational AI research with real-world healthcare applications.
  • Support integration of multimodal AI systems incorporating medical imaging, electronic health records, clinical text, real-time monitoring, and other healthcare data.
  • Translate research prototypes and novel AI methodologies into scalable approaches suitable for real-world clinical environments.
  • Help enable the transition of advanced AI technologies from research into solutions capable of deployment across healthcare institutions worldwide.
  • Advance research in AI safety, reliability, robustness, and interpretability for clinical AI systems.
  • Develop approaches that help ensure foundation models perform reliably across diverse patient populations, clinical environments, and data distributions.
  • Support the development of AI technologies aligned with clinical-grade performance expectations, responsible AI principles, and applicable healthcare requirements.
  • Contribute technical expertise toward evolving healthcare AI regulations and emerging AI safety frameworks.
  • Publish 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.
  • Identify opportunities for novel intellectual property and contribute to patent development.
  • Collaborate with internal and external research communities to advance the state of the art in foundation AI for healthcare.

Desired Qualifications

  • 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.

About the Company

Varian

Tech-enabled radiation oncology hardware and software

Varian, part of Siemens Healthineers, focuses on oncology by providing integrated radiation oncology solutions that combine software, hardware, patient data, and care teams. Its offerings include treatment-planning software, radiation therapy machines, and data-management systems that work together to enable precise cancer treatments. The company differentiates itself through an integrated platform approach and services like Oncology-as-a-Service (OaaS), which lets healthcare providers adopt advanced radiation oncology with ongoing service support rather than large upfront investments. Varian serves healthcare providers such as hospitals and cancer centers, helping them deliver coordinated care and improve patient outcomes. Its goal is to reduce the fear of cancer by using data and technology to support clinicians in delivering effective treatments.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Palo Alto, CA
Founded
1948

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