Model Compression & Edge Deployment Jobs
Computer vision roles requiring Model Compression & Edge Deployment expertise, across all industries and experience levels.
Open Positions
What is Model Compression & Edge Deployment?
Model compression and edge deployment shrink neural networks so they run within the memory, latency and power limits of real hardware. The main levers are quantisation, pruning, knowledge distillation and architecture search, usually applied together and always validated against accuracy loss.
Where Model Compression & Edge Deployment is used
Any camera, vehicle, drone or handset that runs inference locally rather than in the cloud — which is most of them, once bandwidth, privacy or latency enters the requirements.
Roles that ask for Model Compression & Edge Deployment
- Edge AI Engineer
- ML Systems Engineer
- Embedded Vision Engineer
- Deep Learning Engineer
- Inference Optimisation Engineer
Related skills & tools
Model Compression & Edge Deployment jobs — common questions
What is the difference between post-training quantisation and quantisation-aware training?
Post-training quantisation converts an already-trained model, which is fast but can lose accuracy — especially at INT8 and below. Quantisation-aware training simulates quantisation during training so the model adapts, recovering most of the loss at the cost of a retraining cycle.
Is structured or unstructured pruning more useful?
Structured pruning, in practice. Removing whole channels or blocks gives real speedups on standard hardware, whereas unstructured sparsity needs specialised kernel support to translate into anything faster.
Why is this a well-paid specialisation?
It sits between machine learning and systems engineering, and relatively few people are strong at both. Anyone shipping vision on devices needs it, and the constraints are unforgiving, so experienced edge deployment engineers are consistently in demand.