ONNX Jobs in Computer Vision
Browse CV roles that require ONNX across all industries and experience levels.
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What is ONNX?
ONNX is an open format for representing machine learning models, letting a network trained in one framework run in another or on a different runtime. In computer vision it is the usual interchange step between training in PyTorch and deploying through TensorRT, OpenVINO or ONNX Runtime.
Where ONNX is used
Central to any deployment pipeline that separates the training framework from the inference target, which is most of them once the model leaves a GPU server.
Roles that ask for ONNX
- ML Systems Engineer
- Edge AI Engineer
- Computer Vision Engineer
- Inference Optimisation Engineer
- MLOps Engineer
Related skills & tools
ONNX jobs — common questions
What usually goes wrong when exporting to ONNX?
Unsupported or custom operators, dynamic control flow that does not trace, shape inference failures, and subtle numerical differences after conversion. Verifying that ONNX output matches the original framework within tolerance is a standard part of the workflow.
Is ONNX Runtime used directly in production?
Frequently, particularly on CPU and Windows targets and where a single runtime across platforms is preferred. On NVIDIA hardware teams usually convert further to TensorRT for the extra performance.
How much ONNX knowledge do deployment roles expect?
Enough to export cleanly, inspect and modify a graph, resolve operator incompatibilities and apply graph optimisations. It is rarely the headline skill but it is assumed background for edge and inference work.