Computer Vision Engineer
Summary
Build and optimize computer-vision models for production, including dataset creation, annotation tooling, model training, and edge deployment.
- Analyze model failure cases
- Build annotation tooling
- Build evaluation harness
- Collaborate with MLOps and edge engineers
- Create ground truth workflows
- Design dataset creation workflows
- Evaluate edge models
- Fine-tune deep learning models
- Manage training and versioning
- Operationalize machine learning pipeline
- Optimize models for production
- Track model lineage
- Train classification models
- Train mass estimation models
- Train segmentation models