Genentech’s AI Enablement team is building scalable, production-ready machine learning and computer vision systems for large-scale scientific imaging. This onsite role in San Francisco, CA focuses on end-to-end development, from model development and evaluation to web applications, backend pipelines, and cloud infrastructure that support high-throughput biological and clinical image processing.
What you will work on
- Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines for imaging workflows that require throughput and reliable visualization.
- Build, evaluate, and fine-tune state-of-the-art computer vision and deep learning models for tasks such as cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration.
- Create tools to evaluate AI/ML and vision model performance, including establishing benchmark datasets and quantifying model quality, robustness, and drift over time.
- Partner with product managers, bioimage analysts, and research scientists to understand user workflows and translate complex scientific imaging needs into actionable technical specifications.
- Develop and maintain scalable cloud infrastructures and data pipelines to collect, structure, store, and serve large multi-dimensional imaging datasets, including formats such as OME-TIFF, Zarr, and HDF5.
- Optimize GPU memory footprint, batching, and inference throughput to support large image pipelines and deep neural network models.
- Contribute to architectural decisions, participate in code reviews, and help evolve engineering and MLOps processes for computer vision models.
- Be willing to span the full stack and support engineering needs even when they extend beyond core areas of expertise.
- Stay current with emerging computer vision technologies while promoting continuous learning, collaboration, and curiosity.
Required qualifications
- PhD, Master’s with 3–5 years, or Bachelor’s with 4–7 years experience in a related technical field (Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or similar), with focus on machine learning engineering and computer vision.
- Strong proficiency with AI/ML frameworks and computer vision libraries such as PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, and Bio-Formats.
- Deep expertise in computer vision algorithms including CNNs, Vision Transformers, UNet, SAM (Segment Anything), and Cellpose.
- Expert knowledge of machine learning theory, image processing fundamentals, object detection, instance segmentation, and quantitative image analysis.
- Demonstrated experience working with high-dimensional scientific imaging data (for example fluorescence/brightfield microscopy, digital pathology whole slide images, high-content screening, multiplexed imaging).
- Strong knowledge of GPU acceleration and parallel computing, including CUDA and ML performance optimization for large image datasets.
- Experience with Kubernetes, relational and NoSQL databases, object storage, and cloud-native architectures in public clouds (ideally AWS).
- Proven understanding of software engineering best practices, CI/CD, and MLOps principles.
- Excellent communication skills and ability to build trusted partnerships with scientific collaborators, bioimage analysts, and cross-functional teams.
- Ability to quickly learn new technologies and domain knowledge with a passion for continuous learning.
Preferred qualifications
- Experience with multi-modal data fusion, including combining scientific imaging with genomics, transcriptomics, or clinical tabular data.
- Experience working directly with biomedical scientists or in a pharma/biotech research environment.
- Experience with workflow automation, GenAI, Vision-Language Models, and/or AI agents.
Onsite and compensation
- Location: San Francisco, CA (onsite). Onsite presence on the South San Francisco campus is expected for at least 3 days a week.
- Salary range: $168,100 - $312,300 per year, based on the primary location of San Francisco, CA.
- Level: Open to both SE6 and SE7 levels, with final leveling determined after interviews.
- Bonus: A discretionary annual bonus may be available based on individual and Company performance.
- Relocation assistance: Relocation Assistance benefits are not available for this posting.
- Pay determination: Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law.
Technologies you may use
PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, Bio-Formats, CNNs, Vision Transformers, UNet, SAM/Segment Anything, Cellpose, CUDA, Kubernetes, AWS, OME-TIFF, Zarr, HDF5
