ML Computational Scientist

Genentech•Daly City, CA

About The Position

We advance science so that we all have more time with the people we love. Development Sciences (DevSci) is a translational science organization that spans the entire drug discovery and development cycle — from early-stage research to drug commercialization. Within DevSci, the Translational Safety (TS) department is responsible for the preclinical safety evaluation of candidate therapeutic molecules to support their advancement into human studies. The Digital Pathology group within Translational Safety develops and applies computational methods and tools to enhance and extend traditional pathology and toxicology assessments. Our work focuses on extracting quantitative, reproducible, and biologically meaningful information from digital histopathology images and related imaging datasets to support preclinical safety assessment, mechanistic investigation, and translational research. We are seeking a talented PhD-level senior AI/ML computational scientist to join the Digital Pathology group in Translational Safety. We develop computational solutions to biological questions posed by pathologists and toxicologists, with an emphasis on image analysis, machine learning, and deep learning approaches for digital pathology and related imaging data. As part of our team, you will contribute to the development and implementation of image-analysis methods and pipelines using both classical and modern computational approaches. You will work closely with pathologists, toxicologists, and computational scientists to generate quantitative insights from image-based datasets and help advance pathology workflows in drug discovery and development. Opportunity: This role is well suited for a scientist who is excited to apply quantitative and computational methods to biomedical imaging in a highly collaborative, interdisciplinary environment.

Requirements

PhD in biomedical engineering, bioengineering, computer science, computational biology, bioinformatics, data science, statistics, computer vision, image analysis, or a related quantitative discipline; Senior Scientist level (0-2 years) Principal Scientist (3+ years of experience)
Strong background in image analysis, computer vision, machine learning, and deep learning.
Proficiency in Python for scientific computing and data analysis.
Experience with at least one deep learning framework such as PyTorch or TensorFlow.
Demonstrated ability to develop and implement computational methods or pipelines for image-based data analysis.
Strong quantitative, analytical, and problem-solving skills.
Strong written and verbal communication skills, and the ability to work effectively in cross-disciplinary teams.
Interest in applying computational approaches to biomedical imaging and translational safety questions.

Nice To Haves

Experience with digital pathology, whole-slide image analysis, histology, microscopy, or other biomedical imaging data.
Experience with statistical analysis and data visualization.
Familiarity with version control and collaborative software development practices.
Familiarity with open-source or commercial image-analysis tools such as OpenSlide, QuPath, HALO, Visiopharm, or related platforms.
Experience working in high-performance computing or cloud computing environments.
Experience curating, organizing, or preparing image datasets for model development.
Experience working in collaborative research environments involving both computational and experimental scientists.

Responsibilities

Developing and applying computational image-analysis methods and pipelines using classical image processing, machine learning, and deep learning approaches.
Analyzing digital pathology and related imaging datasets to extract quantitative features, generate interpretable results, and support biologically relevant conclusions.
Collaborating closely with pathologists, toxicologists, and cross-functional scientists to understand project goals, design analytical strategies, and interpret findings in biological context.
Contributing to the development of reusable, well-documented computational workflows and research tools.
Perform data quality assessment, statistical analyses, and data visualization to support scientific decision-making.
Evaluating and implementing emerging computational approaches relevant to digital pathology and image analysis.
Communicating results clearly through presentations, written summaries, and discussions with technical and non-technical stakeholders.
Contributing to a collaborative, rigorous, and scientifically curious team environment.

Stand Out From the Crowd

Upload your resume and get instant feedback on how well it matches this job.

Upload and Match Resume