... 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 ...
... 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 ...
Implement rigorous computational/statistical methods for single-cell and spatial transcriptomics data analysis. * Derive actionable insights from clinical/translational single-cell or in-situ spatial ...
Implement rigorous computational/statistical methods for single-cell and spatial transcriptomics data analysis. * Derive actionable insights from clinical/translational single-cell or in-situ spatial ...
This role involves driving strategies for single-cell and spatial transcriptomics, collaborating with scientists, and influencing clinical decision-making. The ideal candidate holds a PhD and has 5+ ...
This role involves driving strategies for single-cell and spatial transcriptomics, collaborating with scientists, and influencing clinical decision-making. The ideal candidate holds a PhD and has 5+ ...
Computational Scientist 3, Spatial Omics & Computational Pathology
South San Francisco, CA · On-site
$129K - $240K/yr
Develop advanced multi-modal representation learning frameworks to harmonize disparate modalities-fusing morphological features with molecular data (transcriptomics/proteomics) to uncover spatial ...
Computational Scientist 3, Spatial Omics & Computational Pathology
South San Francisco, CA · On-site
$129K - $240K/yr
Develop advanced multi-modal representation learning frameworks to harmonize disparate modalities-fusing morphological features with molecular data (transcriptomics/proteomics) to uncover spatial ...
Computational Scientist 3, Spatial Omics & Computational Pathology
South San Francisco, CA · On-site
$129K - $240K/yr
Develop advanced multi-modal representation learning frameworks to harmonize disparate modalities-fusing morphological features with molecular data (transcriptomics/proteomics) to uncover spatial ...
Computational Scientist 3, Spatial Omics & Computational Pathology
South San Francisco, CA · On-site
$129K - $240K/yr
Develop advanced multi-modal representation learning frameworks to harmonize disparate modalities-fusing morphological features with molecular data (transcriptomics/proteomics) to uncover spatial ...
Scientist II, Clinical Bioinformatics
Pleasanton, CA · On-site
$123K - $167K/yr
Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples. To Be Successful, You Will Need: * Ph.D. in bioinformatics, computational ...
Scientist II, Clinical Bioinformatics
Pleasanton, CA · On-site
$123K - $167K/yr
Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples. To Be Successful, You Will Need: * Ph.D. in bioinformatics, computational ...
Scientist II, Clinical Bioinformatics
Pleasanton, CA · On-site
$123K - $167K/yr
Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples. To Be Successful, You Will Need: * Ph.D. in bioinformatics, computational ...
Scientist II, Clinical Bioinformatics
Pleasanton, CA · On-site
$123K - $167K/yr
Process and analyze single-cell or in-situ spatial transcriptomics datasets spanning hundreds to thousands of samples. To Be Successful, You Will Need: * Ph.D. in bioinformatics, computational ...
Principal Scientist, Bioinformatics
South San Francisco, CA · Hybrid
$180K - $215K/yr
A central focus of this role is to drive and execute on Nkarta's strategy for single‑cell and spatial transcriptomics, leveraging scRNA‑seq, spatial, and functional genomics approaches to drive ...
Principal Scientist, Bioinformatics
South San Francisco, CA · Hybrid
$180K - $215K/yr
A central focus of this role is to drive and execute on Nkarta's strategy for single‑cell and spatial transcriptomics, leveraging scRNA‑seq, spatial, and functional genomics approaches to drive ...
A central focus of this role is to drive and execute on Nkarta's strategy for single-cell and spatial transcriptomics, leveraging scRNA-seq, spatial, and functional genomics approaches to drive ...
A central focus of this role is to drive and execute on Nkarta's strategy for single-cell and spatial transcriptomics, leveraging scRNA-seq, spatial, and functional genomics approaches to drive ...
Scientist 2-Spatial Biology and Biophysics
Pleasanton, CA · On-site
$39.25 - $49.25/hr
This position necessitates a deep understanding of molecular biology as you will be applying your skills and knowledge to optimize and evaluate new assays in the area of spatial transcriptomics.
Scientist 2-Spatial Biology and Biophysics
Pleasanton, CA · On-site
$39.25 - $49.25/hr
This position necessitates a deep understanding of molecular biology as you will be applying your skills and knowledge to optimize and evaluate new assays in the area of spatial transcriptomics.
Principal Scientist, Bioinformatics
South San Francisco, CA · On-site
$180K/yr
A central focus of this role is to drive and execute on Nkarta's strategy for single‑cell and spatial transcriptomics, leveraging scRNA‑seq, spatial, and functional genomics approaches to drive ...
Principal Scientist, Bioinformatics
South San Francisco, CA · On-site
$180K/yr
A central focus of this role is to drive and execute on Nkarta's strategy for single‑cell and spatial transcriptomics, leveraging scRNA‑seq, spatial, and functional genomics approaches to drive ...
Deep expertise in single-cell and spatial transcriptomics analysis, including scRNA-seq and related high-dimensional omics datasets * Strong experience analyzing functional genomics and ...
Deep expertise in single-cell and spatial transcriptomics analysis, including scRNA-seq and related high-dimensional omics datasets * Strong experience analyzing functional genomics and ...
Biomedical Specialist - Genomics Core
West Hollywood, CA · On-site
$75K - $117K/yr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
Biomedical Specialist - Genomics Core
West Hollywood, CA · On-site
$75K - $117K/yr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
Biomedical Specialist - Genomics Core
Beverly Hills, CA · On-site
$75K - $117K/yr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
Biomedical Specialist - Genomics Core
Beverly Hills, CA · On-site
$75K - $117K/yr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
... spatial transcriptomics, or multiomics platforms; experience building or scaling these capabilities in an organizational context is strongly preferred Strong publication and/or patent record ...
... spatial transcriptomics, or multiomics platforms; experience building or scaling these capabilities in an organizational context is strongly preferred Strong publication and/or patent record ...
Biomedical Specialist - Genomics Core
West Hollywood, CA · On-site
$21.75 - $29.75/hr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
Biomedical Specialist - Genomics Core
West Hollywood, CA · On-site
$21.75 - $29.75/hr
We specialize in single cell omics and spatial transcriptomics via the 10x Genomics, Parse Biosciences, Mission Bio, and NanoString platforms. Our R&D team offers sample preparation for single cell ...
Biomedical Technician - Genomics Core
West Hollywood, CA · On-site
$28.50 - $37.75/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Biomedical Technician - Genomics Core
West Hollywood, CA · On-site
$28.50 - $37.75/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Computational Biologist
San Francisco, CA · On-site
$125K - $185K/yr
Apply cutting-edge techniques, including scRNAseq, spatial transcriptomics, and long-read sequencing, to derive meaningful insights from complex patient datasets. * Deliver patient-facing results.
Computational Biologist
San Francisco, CA · On-site
$125K - $185K/yr
Apply cutting-edge techniques, including scRNAseq, spatial transcriptomics, and long-read sequencing, to derive meaningful insights from complex patient datasets. * Deliver patient-facing results.
Biomedical Technician - Genomics Core
Los Angeles, CA · On-site
$27.25 - $36/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Biomedical Technician - Genomics Core
Los Angeles, CA · On-site
$27.25 - $36/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Biomedical Technician - Genomics Core
Sacramento, CA · On-site
$27 - $35.75/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Biomedical Technician - Genomics Core
Sacramento, CA · On-site
$27 - $35.75/hr
This includes familiarity with techniques such as single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, multiplex imaging, and other high-dimensional molecular profiling platforms. A ...
Spatial Transcriptomics information
What is spatial transcriptomics?
Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.
What are some common challenges faced by professionals working in spatial transcriptomics, and how can they be addressed?
Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.
What are the key skills and qualifications needed to thrive as a Spatial Transcriptomics Scientist, and why are they important?
To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in California? For Spatial Transcriptomics jobs in California, the most frequently searched job titles are:
- Entry Level Clinical Laboratory Scientist
- Internship Mining Laboratory Technician
- Bachelors In Chemistry
- Entry Level Cosmetic Chemist
- Pathology Laboratory Technician
- Internship Wastewater Laboratory Analyst
- Steelhead Management
- Plant Biology Research
- Hla Medical Technologist Histocompatibility
- Work From Home Hla Technologist
What job categories do people searching Spatial Transcriptomics jobs in California look for? The top searched job categories for Spatial Transcriptomics jobs in California are:
What cities in California are hiring for Spatial Transcriptomics jobs? Cities in California with the most Spatial Transcriptomics job openings:
Computational Scientist 3, Spatial Omics & Computational Pathology
South San Francisco, CA • On-site
Full-time
This job post has expired 1 day ago. Applications are no longer accepted.
Genentech rating
8.8
Based on 22 frontline employees who took The Breakroom Quiz
10th of 74 rated pharmaceutical
Job description
Job Summary:
Genentech is at the forefront of advancing spatial omics capabilities within their Research Pathology Department. They are seeking a highly skilled Computational Scientist to lead projects at the intersection of computer vision, advanced machine learning, and computational pathology, focusing on developing models and AI pipelines for spatial omics initiatives.
Responsibilities:
• Serve 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.
Qualifications:
Required:
• 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.
Preferred:
• 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.
Company:
Genentech is a biotechnology research company that specializes in genetic testing and personalized medicines. Founded in 1976, the company is headquartered in South San Francisco, USA, with a team of 10001+ employees. The company is currently Late Stage.
Genentech is at the forefront of advancing spatial omics capabilities within their Research Pathology Department. They are seeking a highly skilled Computational Scientist to lead projects at the intersection of computer vision, advanced machine learning, and computational pathology, focusing on developing models and AI pipelines for spatial omics initiatives.
Responsibilities:
• Serve 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.
Qualifications:
Required:
• 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.
Preferred:
• 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.
Company:
Genentech is a biotechnology research company that specializes in genetic testing and personalized medicines. Founded in 1976, the company is headquartered in South San Francisco, USA, with a team of 10001+ employees. The company is currently Late Stage.
What Genentech employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Genentech
Sourced by ZipRecruiter
A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.
Industry
Scientific research and development services
Company size
10,000+ Employees
Headquarters location
South San Francisco, CA, US
Year founded
1976