1 day ago
Base Salary
$168k - $312k/yr
Responsibilities
- Design, develop, test, and maintain scalable AI/ML scientific web applications and backend pipelines for high-throughput imaging data.
- Build, evaluate, and fine-tune computer vision and deep learning models for cell segmentation, classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration.
- Develop model evaluation tools, benchmark datasets, and methods for measuring model quality, robustness, and drift.
- Partner with product managers, bioimage analysts, research scientists, and scientific collaborators to translate imaging workflows into technical specifications.
- Develop scalable cloud infrastructure and data pipelines for large multidimensional imaging datasets.
- Optimize GPU memory usage, batching, inference throughput, and deep neural network performance.
- Contribute to architecture, code reviews, engineering processes, MLOps, and full-stack development as needed.
Requirements
- PhD, or a Master’s degree with 3–5 years of experience, or a Bachelor’s degree with 4–7 years of experience in Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or a related technical field focused on machine learning engineering and computer vision.
- Strong proficiency with AI/ML frameworks and computer vision libraries including PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, or Bio-Formats.
- Deep expertise in computer vision algorithms such as CNNs, Vision Transformers, UNet, SAM/Segment Anything, and Cellpose.
- Expert knowledge of machine learning theory, image processing, object detection, instance segmentation, and quantitative image analysis.
- Experience with high-dimensional scientific imaging data such as microscopy, digital pathology whole slide images, high-content screening, and multiplexed imaging.
- Knowledge of GPU acceleration, parallel computing, CUDA, and machine learning performance optimization.
- Experience with Kubernetes, relational and NoSQL databases, object storage, and cloud-native architectures, ideally in AWS.
- Understanding and application of software engineering best practices, CI/CD, and MLOps principles.
- Strong communication and collaboration skills with scientific and cross-functional teams.
- Preferred experience with multimodal data fusion, biomedical or pharmaceutical research environments, workflow automation, GenAI, Vision-Language Models, or AI agents.
Benefits
- Expected onsite presence at the South San Francisco campus at least 3 days per week.
- Relocation assistance is not available for this posting.
- A discretionary annual bonus may be available based on individual and company performance.
- Genentech is an equal opportunity employer and provides an accommodation process for applicants with disabilities.
Tech Stack
Categories
About Genentech
Genentech discovers, develops, and commercializes biologic therapies for serious diseases across oncology, immunology, neuroscience, ophthalmology, infectious disease, and metabolism. Founded in 1976 and headquartered in South San Francisco, it operates as a member of the Roche Group, with research centered in its gRED organization and revenue driven by prescription drug sales and collaborations.