Gilead's mission is to discover, develop, and deliver therapies that will improve the lives of patients with life-threatening illnesses worldwide.
Requirements
- Proficiency in deep learning and data science libraries such as PyTorch, Pandas, scikit-learn and NumPy;
- Experience with image processing packages such as OpenSlide, OpenCV, MONAI, or Elastix;
- Extensive experience with DL models and architectures for image segmentation and classification such as ResNet, U-Net, and transformer-based models (e.g., ViT, Swin Transformer);
- Familiarity with other ML algorithms (e.g., Logistic Regression, Random Forest, SVM);
- Experience managing end-to-end ML/DL/AI projects, including data engineering, resource management, model training, selection, evaluation, and stakeholder communication.
Responsibilities
- Analyzing pathology imaging data (e.g., H&E, IHC, CISH, mIF, CODEX, Spatial Transcriptomics) generated across Gilead’s drug development pipeline.
- Developing image analysis tools using commercial, internal, and open-source packages.
- Building automated image analysis pipelines for deployment on-premises and in cloud-based high-performance computing (HPC) environments.
- Supporting the development of advanced analytics, computer vision, and computational tools to derive novel imaging-based biomarker endpoints.
- Collaborating with internal and external scientific partners to design, execute, and validate analytic strategies for tissue-based endpoints and imaging biomarkers supporting Gilead’s drug discovery and development pipelines.
- Evaluating and implementing new computational approaches in digital pathology to extract histopathological endpoints and perform spatial analyses.
Other
- PhD in a relevant quantitative field (e.g., Computer Science, Biomedical Engineering, Physics, Mathematics, Statistics); postdoctoral experience is a plus;
- MS degree in Computer Science/Biomedical Engineering with 4+ years of industry experience;
- BS degree in Computer Science/Biomedical Engineering with 6+ years of industry experience;
- Excellent written and verbal communication skills;
- Ability to multitask and prioritize while maintaining high standards of efficiency and quality.
