Senior AI Engineer- Imaging

The Opportunity

• Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines 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, image registration) applied to biological and clinical imaging modalities.
• 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 user workflows, shape requirements, and translate complex scientific imaging needs into actionable technical specifications.
• Develop and maintain scalable cloud infrastructures and data pipelines for collecting, structuring, storing, and accessing large-scale multi-dimensional imaging datasets (e.g., OME-TIFF, Zarr, HDF5).
• 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.
• Be willing to span the full stack and contribute where needed, even outside of core areas of expertise.
• Stay up-to-date with emerging computer vision technologies and industry best practices while promoting a culture of continuous learning, collaboration, and curiosity.

Who You Are

• PhD, Master's with 3-5 years, or Bachelor's with 4-7 years experience with a degree in Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or a related technical field with focus on machine learning engineering and computer vision.
• 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).
• 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).
• 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.

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.