Image Segmentation Jobs
Computer vision roles requiring Image Segmentation expertise, across all industries and experience levels.
Open Positions
What is Image Segmentation?
Image segmentation splits a picture into meaningful regions by labelling every pixel — road versus pavement, tumour versus healthy tissue, product versus conveyor belt. It is the workhorse of applied computer vision, because most downstream decisions need to know not just that something is present but exactly where its boundaries fall.
Where Image Segmentation is used
Radiology and pathology teams lean on it hardest, but any system that has to measure area, follow a boundary, or separate touching objects ends up with a segmentation model at its core.
Roles that ask for Image Segmentation
- Computer Vision Engineer
- Medical Imaging Scientist
- Perception Engineer
- Deep Learning Engineer
- Research Scientist, Vision
Related skills & tools
Image Segmentation jobs — common questions
What is the difference between semantic and instance segmentation?
Semantic segmentation labels every pixel with a class but does not separate individual objects — all cars share one label. Instance segmentation distinguishes each object separately, so three cars get three distinct masks. Panoptic segmentation combines both.
Which architectures should I know for segmentation roles?
U-Net remains the default in medical imaging and is worth knowing cold. Beyond that, DeepLabv3+, Mask2Former and SAM-style promptable models come up regularly, along with the loss functions that matter in practice — Dice, focal, and boundary losses for handling class imbalance.
Do segmentation roles require a PhD?
Rarely. Research positions at large labs often prefer one, but the majority of segmentation work is applied engineering — building annotation pipelines, training and fine-tuning models, and getting them to hold up on real data. Strong practical experience usually outweighs the credential.