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Machine Learning Scientist – Digital Pathology
| Company | Tempus |
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| Location | San Francisco, CA, USA |
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| Salary | $120000 – $160000 |
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| Type | Full-Time |
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| Degrees | Master’s, PhD |
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| Experience Level | Mid Level |
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Requirements
- Ph.D. (or MS and 2+ years of working experience) in Computer Science, Artificial Intelligence, Data Science, Computational Biology, or a related field. A strong academic background with a focus on imaging for healthcare applications is preferred.
- Solid knowledge of machine learning concepts, including deep learning, optimization algorithms, regularization techniques, weakly supervised learning and self-supervised learning.
- Extensive experience in developing and implementing imaging AI models, such as ViT, CNNs, Unet, and related architectures including tasks such as classification, segmentation or detection.
- Outstanding analytical and problem-solving skills, with a particular focus on understanding the intricacies of multi-modal medical datasets.
- Proficiency in programming languages and packages commonly used in AI research, such as Python and PyTorch / Tensorflow.
- Strong collaboration and communication abilities.
- Track record in publications, patents, and/or launched products in this space.
Responsibilities
- Develop and Implement AI/ML Models: Design, train, and validate machine learning models for analyzing digital pathology images (whole-slide images) to perform tasks like biomarkers and outcomes prediction.
- Algorithm Validation and Deployment: Validate ML/AI algorithms analytically and/or clinically, and deploy them into existing workflows or production environments.
- Data Analysis and Curation: Work with large-scale pathology datasets, ensuring data quality and integrity, collecting annotations, and preprocessing for model training and evaluation.
- Collaboration: Collaborate with a multidisciplinary team including pathologists, biologists, statisticians, data analysts, and engineers to integrate AI solutions and transform health care.
- Research and Innovation: Stay updated on the latest advancements in AI, machine learning, digital pathology, and multi-modal healthcare to contribute to ongoing projects and identify new opportunities.
- Biomarker Discovery: Assist in identifying biomarkers from histopathology and spatial omics data to support patient stratification and companion diagnostic efforts.
Preferred Qualifications
- Experience in pre-training and fine-tuning unimodal or multimodal foundation models.
- Experience working with histopathology images, DNA/RNA molecular sequencing data or clinical data (structured EHRs, clinical notes).
- Experience working in an industry setting.
