Sr Associate Image Analysis And Digital Pathology

Job Description:

At Amgen, our shared mission—to serve patients—drives all that we do. It is key to our becoming one of the world’s leading biotechnology companies. We are global collaborators who achieve together—researching, developing, and delivering ever-better products that reach over 10 million patients worldwide.

We are seeking a highly motivated Senior Associate– Image Analysis & Digital Pathology to join our Computational Imaging and Digital Biomarkers team. The successful candidate will contribute to the development and application of computational pathology and AI/ML methods to support translational research, biomarker discovery, and precision medicine initiatives across therapeutic areas.

The candidate will work closely with pathologists, data scientists, translational scientists, and software engineers to build scalable image analysis workflows and generate quantitative insights from complex imaging datasets including whole-slide pathology images and spatial biology platforms.

Live – What You Will Do

In this vital role, you will:

• Develop and apply image analysis, computer vision, and machine learning methods for digital pathology datasets.
• Design and implement analysis pipelines for whole-slide imaging (WSI), H&E, IHC, multiplex immunofluorescence, and spatial omics datasets.
• Build reproducible and scalable workflows using Python and modern ML/DL frameworks such as PyTorch or TensorFlow.
• Perform tissue segmentation, cell phenotyping, feature extraction, and spatial analysis using state-of-the-art computational approaches.
• Collaborate with pathologists and translational teams to interpret image-derived biomarkers and biological findings.
• Contribute to evaluation, validation, and benchmarking of image analysis algorithms and workflows.
• Support deployment and operationalization of computational imaging pipelines in cloud or high-performance computing environments.
• Stay current with advances in computational pathology, AI/ML, and spatial biology technologies.
  
  

• PhD or MS in Computer Science, Biomedical Engineering, Computational Biology, Bioinformatics, Electrical Engineering, Applied Mathematics, or related quantitative discipline.
• Strong background in computer vision, image analysis, and machine learning/deep learning.
• Demonstrated experience working with biomedical imaging data (pathology preferred).
• Proven track record in developing, validating, and deploying advanced computer vision and deep learning algorithms, including contributions to released products.
• Proficiency in Python and common ML frameworks (e.g., TensorFlow, PyTorch).
• Deep understanding of image analysis methods such as segmentation, object detection, and classification, ideally within digital pathology (e.g., histopathology, tissue imaging) and/or spatial omics.
• Familiarity with large-scale data management and cloud-based computational environments.
• Strong communication and interpersonal skills
  
  

• 3+ years of experience in computational imaging, digital pathology, or medical image analysis.
• Strong programming skills in Python and experience with scientific computing libraries.
• Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
• Experience analyzing pathology imaging modalities including H&E, IHC, multiplex IF, or spatial transcriptomics data.
• Familiarity with whole-slide imaging tools and digital pathology platforms such as QuPath, HALO, Visiopharm, or OpenSlide.
• Understanding of machine learning methods for segmentation, classification, detection, and representation learning.
• Strong communication and collaboration skills in multidisciplinary scientific environments.
• Track record of scientific publications, conference presentations, or applied translational research contributions is preferred.
  
  

• A collaborative and diverse global team environment
• Opportunities to work with cutting-edge technologies in biomedical research
• Training and mentoring to support your career path
• Flexibility in work arrangements and work-life balance