Senior Scientist II, Computational Pathology, Precision Medicine Pathology

AbbVie•South San Francisco, CA

7h•$109,500 - $208,500•Onsite

About The Position

AbbVie Precision Medicine Pathology organization is committed to driving tissue based translational and biomarker efforts for our pre-clinical and clinical stage programs. We are seeking a talented and motivated Machine Learning (ML) Scientist to develop and apply advanced Artificial Intelligence (AI) techniques for analyzing complex histopathology and spatial omics datasets. This is a hands-on role ideal for candidates who are passionate about learning, collaborating, and driving innovation in the exciting intersection of machine learning, digital pathology, and precision medicine. As a computational pathology scientist, you will be an integral part of a highly cross-functional team, working closely with colleagues from pathology laboratories and collaborating with research pathologists and assay scientists. You will engage with investigators involved in both discovery and late-stage research across a spectrum of disease areas, including oncology, cancer immunotherapy, immunology, and neuroscience, leveraging your AI expertise to advance our team's research objectives.

Requirements

Ph.D. in Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, or related field with an emphasis on computer vision or machine learning; OR M.S. with 5+ years of relevant industry experience.
Experience in image analysis techniques, including segmentation, object detection, and classification, evidenced by publications, open-source projects, or product development.
Proficiency in programming languages like python and demonstrated experience using computer vision libraries such as OpenCV and ML frameworks like TensorFlow and PyTorch.
Familiarity with MLOps practices, including deployment, monitoring, and lifecycle management of machine learning models in production environments.
Familiarity with cloud computing platforms and scalable AI/ML pipelines (e.g., AWS, Azure, GCP).
Excellent communication skills, including the ability to contribute to collaborative projects and explain technical concepts to interdisciplinary teams.
Strong problem-solving skills and demonstrated creative approaches to overcoming challenges.

Nice To Haves

Exposure to digital pathology or biomedical imaging, such as histopathology, microscopy, or tissue imaging datasets.
Experience with spatial omics data or integrating molecular data with image analysis.
Working knowledge of techniques in precision medicine, biomarker discovery, or personalized treatment strategies is a plus.
Familiarity with cell and molecular biology concepts in fields like oncology, immunology, or cancer immunotherapy, or an eagerness to learn.

Responsibilities

Develop, train, and validate machine learning models for tissue image analysis, including segmentation, object detection, and classification.
Apply advanced techniques such as deep learning and representation learning to solve key challenges in digital pathology.
Curate and maintain large-scale pathology datasets, ensuring data quality and integrity for robust model training and evaluation.
Develop and implement tools and pipelines for data preprocessing, feature engineering, and model deployment.
Collaborate with pathologists, biologists, statisticians, data analysts, and fellow engineers to integrate machine learning solutions into existing workflows.
Assist in external collaborations with research partners to enhance project outcomes and foster innovation.
Assist in evaluating histopathology and spatial omics datasets to identify biomarkers that inform patient stratification and companion diagnostic efforts.
Stay updated on the latest developments in AI, machine learning, and digital pathology techniques, and bring these insights to ongoing projects.