Computer Vision Engineer
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- Build vision and deep-learning foundations
- Build and evaluate CNN-based vision models
- Ship modern perception systems to production
Benchmark: Cross-industry computer-vision and perception-systems signals (current Oct 2026) Mastery ladder: 2 current-market competencies per level; 2 substantive learning bridges per competency (12 total).
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Build vision and deep-learning foundations
Computer vision and deep-learning fundamentals
Coverage: Computer Vision / Deep Learning Fundamentals
Image classification and neural-network basics
Coverage: Image Classification / Neural Networks / Optimization
Build and evaluate CNN-based vision models
Backpropagation and convolutional networks
Coverage: Backpropagation / Convolutional Networks
CNN architectures and training practice
Coverage: CNN Architectures / Model Training / Regularization
Ship modern perception systems to production
Advanced training, detection and segmentation
Coverage: Object Detection / Segmentation / Model Training
3D vision, numerical foundations and deployment
Coverage: 3D Vision / Numerical Computing / Production Deployment
Role-Readiness Proof / Capstone
Build a detection/segmentation pipeline for an open image dataset with labeled test splits: benchmark mAP, IoU, precision/recall, inference latency, model size and subgroup errors; deploy a versioned inference API with monitoring; and defend architecture and cost tradeoffs.
Proof Rubric
Demonstrate vision-model fundamentals through production judgment: disciplined data/labeling practice, defensible architecture choices, rigorous evaluation (mAP/IoU/precision/recall), and deployment readiness covering latency, drift and robustness.