Genentech discovers, develops, manufactures and commercializes treatments for patients with serious and life-threatening medical conditions. Backed by North Carolina Biotechnology Center.
About the role
· Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines tailored for high-throughput scientific imaging data processing and visualization. · Build, evaluate, and fine-tune state-of-the-art computer vision and deep learning models (e.g., cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration) applied to biological and clinical imaging modalities. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong proficiency with AI/ML frameworks and computer vision libraries (e.g., PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, or Bio-Formats)
- Deep expertise in computer vision algorithms (e.g., CNNs, Vision Transformers, UNet, SAM/Segment Anything, 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 (e.g., fluorescence/brightfield microscopy, digital pathology whole slide images, high-content screening, multiplexed imaging, etc)
- Strong knowledge of GPU acceleration, parallel computing, 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)
More about this role
We advance science so that we all have more time with the people we love.
As an imaging-focused machine learning engineer within the AI Enablement team, you will be working closely with folks that span the gamut across computational scientists, biologists, bench scientists, AI/ML experts, product leaders, software engineers, and everyone in between. Your work will help bridge scientific intuition with quantitative analysis, enable richer phenotypic analysis, and provide new multimodal capabilities across large-scale, state-of-the-art datasets. You'll build, own, and constantly improve scalable AI/ML and computer vision systems that unlock the potential of our diverse scientific imaging data (e.g., microscopy, digital pathology, biomedical imaging, etc), accelerating the discovery and development of life-changing treatments for patients.
· Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines tailored for high-throughput scientific imaging data processing and visualization.
· Build, evaluate, and fine-tune state-of-the-art computer vision and deep learning models (e.g., cell segmentation, cell classification, phenotypic...
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