Senior AI Engineer- Imaging

Genentech is hiring an imaging-focused AI/ML Engineer to build and continuously improve scalable computer vision systems for large-scale scientific imaging data.

Responsibilities

• Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines for high-throughput imaging processing and visualization.
• Build, evaluate, and fine-tune state-of-the-art computer vision and deep learning models for scientific imaging use cases 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, define benchmark datasets, and quantify model quality, robustness, and drift over time.
• Partner with product managers, bioimage analysts, and research scientists to understand user workflows, shape requirements, and translate scientific imaging needs into technical specifications.
• Develop and maintain scalable cloud infrastructure and data pipelines to collect, structure, store, and enable access to large-scale multi-dimensional imaging datasets including OME-TIFF, Zarr, and HDF5.
• Optimize GPU memory usage, batching, and inference throughput for large image pipelines and deep neural networks.
• Participate in architectural decisions, code reviews, and the evolution of engineering and MLOps practices for computer vision systems.
• Span the full stack and contribute beyond core expertise when needed.
• Stay current on emerging computer vision technologies and best practices, and support a culture of continuous learning and collaboration.

Requirements

• PhD, Master’s (3–5 years), or Bachelor’s (4–7 years) in a related technical field with focus on machine learning engineering and computer vision (Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or similar).
• Strong proficiency with AI/ML frameworks and computer vision libraries, including PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, and/or Bio-Formats.
• Deep expertise in computer vision algorithms such as CNNs, Vision Transformers, UNet, SAM (Segment Anything), and Cellpose.
• Expertise in machine learning theory and image processing fundamentals including object detection, instance segmentation, and quantitative image analysis.
• Demonstrated experience 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 databases, NoSQL databases, object storage, and cloud-native architectures in public clouds (ideally AWS).
• Proven software engineering best practices, including CI/CD and MLOps principles.
• Excellent communication skills and ability to build trusted partnerships with scientific collaborators and cross-functional teams.
• Ability to rapidly learn new technologies, domain knowledge, and programming languages with a focus on continuous learning.

Technologies

• 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

Preferred Qualifications

• Experience with multi-modal data fusion 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 Presence

• Onsite presence on the South San Francisco campus expected at least 3 days per week.

Relocation Assistance

• Relocation assistance is not available for this posting.

Compensation

• Expected salary range: $168,100 - $312,300 annually (San Francisco, CA primary location).
• Open to SE6 and SE7 levels; final leveling determined after the interview process.
• A discretionary annual bonus may be available based on individual and Company performance.
• Actual pay determined based on experience, qualifications, geographic location, and other factors permitted by law.