Siamese Networks & Metric Learning Jobs
Computer vision roles requiring Siamese Networks & Metric Learning expertise, across all industries and experience levels.
Open Positions
What is Siamese Networks & Metric Learning?
Siamese networks and metric learning train models to produce embeddings where similar items sit close together and dissimilar items far apart. Instead of classifying into fixed categories, they learn a similarity function — which is what you need when the set of classes is open-ended or changes constantly.
Where Siamese Networks & Metric Learning is used
Visual product search, face and person re-identification, signature and defect verification, and any system that must recognise items it was never explicitly trained on.
Roles that ask for Siamese Networks & Metric Learning
- Machine Learning Engineer
- Computer Vision Engineer
- Applied Scientist
- Search and Recommendations Engineer
- Research Scientist, Vision
Related skills & tools
Siamese Networks & Metric Learning jobs — common questions
Why use metric learning instead of classification?
Because adding a new class should not require retraining. A retailer adding products daily, or a security system enrolling new individuals, needs a model that compares against a gallery rather than one with a fixed output layer.
What losses are standard?
Contrastive and triplet loss as the classical pair, with hard negative mining being the part that actually determines whether training works. ArcFace and CosFace dominate face recognition, and InfoNCE bridges into self-supervised learning.
What does deployment involve?
Usually an approximate nearest-neighbour index — FAISS, ScaNN or a vector database — sitting behind the embedding model. These roles often blend computer vision with search infrastructure.