Senior, AI Engineer - (Imaging)

Description

The AI Enablement team at the company builds machine learning and computer vision tools for scientific imaging.

You will work alongside computational scientists, biologists, bench scientists, AI and ML specialists, product leads and software engineers.

Your role connects scientific reasoning with quantitative analysis, supports richer phenotypic analysis and adds multimodal capabilities across large, cutting-edge datasets.

You will help turn diverse imaging data, such as microscopy, digital pathology and biomedical imaging, into faster discovery and development of treatments for patients.

The company is a large global healthcare organisation with over 100,000 employees.

Responsibilities

• Design, build and test scalable AI/ML web applications and backend pipelines for processing and visualising high-throughput scientific imaging data.
• Build, evaluate and fine-tune modern computer vision and deep learning models for tasks such as cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction and image registration on biological and clinical images.
• Create tools that measure model performance, set up benchmark datasets, and track model quality, robustness and drift over time.
• Build and maintain scalable cloud infrastructure and data pipelines that collect, structure, store and serve large multi-dimensional imaging datasets in formats such as OME-TIFF, Zarr and HDF5.
• Shape architecture decisions, review code, and help evolve engineering and MLOps practices for computer vision models.
• Keep up with new computer vision methods and industry practice, and share what you learn with the team.

Requirements

• A PhD, or a Master's degree with 3 to 5 years of experience, or a Bachelor's degree with 4 to 7 years of experience, in computer science, biomedical engineering, electrical engineering, computational biology or a related technical field, with a focus on machine learning engineering and computer vision.
• Strong hands-on skill with AI/ML frameworks and vision libraries such as PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari or Bio-Formats.
• Deep understanding of computer vision algorithms such as CNNs, Vision Transformers, UNet, SAM (Segment Anything) and Cellpose.
• Expert knowledge of machine learning theory, image processing basics, object detection, instance segmentation and quantitative image analysis.
• Proven experience with high-dimensional scientific imaging data, such as fluorescence or brightfield microscopy, whole slide pathology images, high-content screening and multiplexed imaging.
• Experience with Kubernetes, relational and NoSQL databases, object storage and cloud-native architectures, ideally on AWS.
• Excellent communication skills and a track record of building trusted working relationships with scientists, bioimage analysts and cross-functional teams.
• Nice to have: experience fusing multimodal data, such as imaging with genomics, transcriptomics or clinical tabular data, or working in a pharma or biotech research setting.
• Nice to have: experience with workflow automation, GenAI, Vision-Language Models and/or AI agents.

Conditions

The company is an equal opportunity employer.