The Opportunity
The Research Pathology Department, an integral part of Genentech’s Research and Early Development Organization (gRED), is dedicated to ensuring that strategies for the treatment of diseases are grounded in accurate analyses of pathogenetic mechanisms. Building upon a strong foundation in digital pathology, the department is at the forefront of advancing spatial omics capabilities, integrating cutting-edge, tissue-based technologies with computational methods to enable high-resolution spatial profiling of biological systems. DPIA-SO (Digital Pathology Image Analysis-Spatial Omics) is a specialized team within Research Pathology focused on collaborative spatial omics computational analysis.
We are seeking a highly skilled Computational Scientist to join our team, operating at the intersection of computer vision, advanced machine learning, computational pathology, and spatial biology. The core focus is to develop models, digital pathology infrastructure, and AI pipelines needed to power spatial omics initiatives and scientifically driven projects. We welcome individuals from computational pathology and computer vision backgrounds who possess highly transferrable skills. Candidates with direct spatial omics machine learning expertise represent an ideal fit.
Key ResponsibilitiesServe as the Technical Lead for computer vision, AI/ML, and multiplex imaging projects, architecting pipelines for standard whole-slide images (H&E, IHC) and high-dimensional spatial omics data.
Design and implement cutting-edge machine learning algorithms, including foundation models and generative architectures, for image segmentation, feature extraction, and predictive modeling.
Develop advanced multi-modal representation learning frameworks to harmonize disparate modalities—fusing morphological features with molecular data (transcriptomics/proteomics) to uncover spatial niches and cell-cell interactions.
Engineer scalable imaging data infrastructure for large-scale image storage (e.g., OME-ZARR, OME-TIFF, SpatialData) on HPC and cloud environments.
Embed biological priors—such as known metabolic pathways or spatial knowledge graphs—directly into the mathematical design of the AI models.
Collaborate closely with pathologists, wet-lab, and dry-lab researchers to interpret data, visualize results, and contribute to upstream experimental design.
Ph.D in Computational Biology, Computer Science, Machine Learning, Imaging Science, Data Science, or a related highly quantitative field or a Masters Degree in these fields with 3+ years of experience may be considered.
Demonstrated experience in computer vision, deep learning, or image processing, specifically with tissue-based or high-dimensional imaging data.
Strong foundation in digital/computational pathology workflows and/or advanced machine learning (e.g., probabilistic modeling, representation learning, generative modeling).
Deep proficiency in Python software engineering and extensive hands-on experience with modern machine learning frameworks (e.g., PyTorch, TensorFlow, JAX).
Excellent problem-solving skills with the ability to work independently as a technical lead in a multidisciplinary environment.
Demonstrated experience applying machine learning to single-cell spatial transcriptomics and/or spatial proteomics analysis (e.g., 10X Genomics Xenium, Visium, Lunaphore COMET).
Hands-on experience with multi-modal data integration, specifically combining spatial transcriptomics, proteomics, and histology datasets.
Familiarity with the scverse ecosystem (e.g., Scanpy, Squidpy, SpatialData, scVI), computer vision libraries (OpenCV, scikit-image), and modern cloud infrastructure.
Solid understanding of tissue histology, cell biology, and tumor microenvironments to inform model architecture.
Experience developing agentic AI systems, LLM-driven autonomous workflows, or advanced AI-oriented tools for complex biological datasets.
Relocation benefits are available for this posting.
The expected salary range for this position based on the primary location of South San Francisco, CA is $129,200 - $240,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
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Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
Skills Required
- Ph.D in Computational Biology, Computer Science, Machine Learning, Imaging Science, Data Science or related field
- Demonstrated experience in computer vision, deep learning, or image processing with imaging data
- Strong foundation in digital/computational pathology workflows or advanced machine learning
- Deep proficiency in Python software engineering and experience with machine learning frameworks
- Excellent problem-solving skills and ability to work independently in multidisciplinary environments
Genentech Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Genentech and has not been reviewed or approved by Genentech.
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Healthcare Strength — Health coverage is described as comprehensive across medical, dental, vision, mental health, and prescriptions, supported by HSAs/FSAs and broad wellness resources. On‑site fitness and health centers, mental‑health clinicians, and specialized programs like fully covered preventive cancer screenings and menopause support deepen the offering.
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Retirement Support — Retirement benefits feature a 401(k) with up to a 4% company match plus an additional annual 6% company contribution to eligible pay. Additional financial protections such as life and accident insurance complement salary, bonuses, and stock options.
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Leave & Time Off Breadth — Time away includes about 20 paid vacation days, paid holidays, personal days, and a year‑end shutdown. A paid six‑week sabbatical every six years notably expands long‑term time‑off flexibility.
Genentech Insights
What We Do
Considered the founder of the industry, Genentech, now a member of the Roche Group, has been delivering on the promise of biotechnology for more than 40 years. Genentech is a biotechnology company dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life-threatening diseases. Our transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis. We're passionate about finding solutions for people facing the world's most difficult-to-treat conditions. That is why we use cutting-edge science to create and deliver innovative medicines around the globe. To us, science is personal. Making a difference in the lives of millions starts when you make a change in yours.
