Vision Transformers (ViT) Jobs
Computer vision roles requiring Vision Transformers (ViT) expertise, across all industries and experience levels.
Open Positions
What is Vision Transformers (ViT)?
Vision transformers apply the self-attention architecture to images by treating patches as tokens, giving models a global receptive field from the first layer. Since 2020 they have become the dominant backbone for large-scale vision, and the foundation for multimodal models.
Where Vision Transformers (ViT) is used
Anywhere large pre-trained backbones are fine-tuned for a downstream task, which now includes most well-funded vision teams. Their appetite for data and compute makes them a poor fit for small, constrained problems.
Roles that ask for Vision Transformers (ViT)
- Deep Learning Engineer
- Research Scientist, Vision
- Machine Learning Engineer
- Applied Scientist
- Foundation Model Engineer
Related skills & tools
Vision Transformers (ViT) jobs — common questions
Have transformers replaced CNNs in vision?
Not universally. ViTs lead at large data and compute scales, but CNNs remain stronger with limited data and are far cheaper at the edge. ConvNeXt showed much of the gap was training recipe rather than architecture. Production teams still deploy plenty of convolutional models.
Which variants should I know?
The original ViT, Swin for its hierarchical windowed attention, DINOv2 for self-supervised features, and SAM for promptable segmentation. Understanding why patching and positional encoding are designed as they are matters more than the model zoo.
Do these roles require large-scale training experience?
Often yes at research labs — distributed training, mixed precision, and the practicalities of multi-node jobs. Applied roles more commonly fine-tune existing checkpoints, which is a very different day-to-day.