Senior AI Engineer- Imaging
About The Position
As an imaging-focused machine learning engineer on the AI Enablement team, you will collaborate with computational scientists, biologists, bench scientists, AI/ML experts, product leaders, software engineers, and other partners. The role bridges scientific intuition with quantitative analysis, enables richer phenotypic analysis, and provides multimodal capabilities across large-scale scientific datasets. You will build, own, and improve scalable AI/ML and computer vision systems for scientific imaging—including microscopy, digital pathology, and biomedical imaging—to help accelerate the discovery and development of treatments. The position involves designing and maintaining scientific web applications and backend pipelines; developing computer vision and deep learning models; evaluating model quality, robustness, and drift; and building scalable cloud infrastructure and data pipelines for large imaging datasets. You will optimize GPU use and inference throughput, contribute to architecture and engineering practices, and partner with product and research collaborators to translate scientific workflows into technical solutions. Onsite presence at the South San Francisco campus is expected at least three days per week.
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 such as 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 fundamentals, object detection, instance segmentation, and quantitative image analysis.
- Demonstrated experience with high-dimensional scientific imaging data, such as fluorescence or brightfield microscopy, digital pathology whole slide images, high-content screening, or multiplexed imaging.
- Strong knowledge of GPU acceleration, parallel computing, 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; AWS is preferred in the posting.
- Proven understanding and application 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 acquire new technologies, domain knowledge, and programming languages, with a passion for continuous learning.
Nice To Haves
- Experience with multimodal 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.
Responsibilities
- Design, develop, and test robust, scalable, maintainable AI/ML scientific web applications and backend pipelines for high-throughput imaging data processing and visualization.
- Build, evaluate, and fine-tune computer vision and deep learning models for tasks such as cell and tissue segmentation, cell classification, phenotypic profiling, feature extraction, and image registration.
- Build tools to evaluate AI/ML and vision model performance, establish benchmark datasets, and quantify model quality, robustness, and drift over time.
- Partner with product managers, bioimage analysts, and research scientists to understand workflows, shape requirements, and translate scientific imaging needs into technical specifications.
- Develop and maintain scalable cloud infrastructure and data pipelines for collecting, structuring, storing, and accessing large-scale multidimensional imaging datasets.
- Optimize GPU memory footprint, batching, and inference throughput for large image pipelines and deep neural network models.
- Contribute to architectural decisions, code reviews, and the evolution of engineering and MLOps processes for computer vision models.
- Contribute across the full stack and outside core areas of expertise as needed.
- Keep up with emerging computer vision technologies and industry best practices, while supporting continuous learning, collaboration, and curiosity.
Benefits
- A discretionary annual bonus may be available based on individual and Company performance.
- Benefits are referenced, but specific benefits are not listed in the supplied posting.
- Relocation assistance is not available for this posting.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
