Research Engineer / Research Scientist, Vision Foundation Models

What you'll do

• Design, pretrain, and scale models for X-rays, CT, and MRI, including 3D volumetric imaging.
• Develop learning methods, architectures, training objectives, and evaluations for clinically meaningful visual representations.
• Lead research from hypothesis and experimentation through distributed training, evaluation, and deployment handoff; publish findings.

What they ask for

• Deep expertise in computer vision, visual representation learning, or large-scale vision foundation model pretraining.
• Demonstrated ability to develop, train, or improve state-of-the-art computer vision models.
• Strong understanding of modern vision architectures and self-supervised learning methods, including contrastive learning, masked image modeling, or JEPA-style objectives.
• Experience with large-scale distributed training using PyTorch or JAX; ability to formulate research questions, design experiments, and implement research ideas.

Nice to have

• Publications at leading ML or computer vision conferences such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or MICCAI.
• Experience with 3D or spatiotemporal data, CT/MRI processing, vision encoders, or multimodal learning.

Tools

PyTorch, JAX, ViT, DINO, DINOv2, DINOv3

AI focus

Research and development of vision foundation models for medical imaging, including self-supervised learning, visual representation learning, large-scale pretraining, and 3D volumetric modeling.