SkillHack › Career guides › Computer Vision Engineer
How to become an Computer Vision Engineer
Image and video models, VLMs, detection, and visual inspection. Ship systems that see and understand the world.
- 5phases in the roadmap
- 15topics to work through
- 75graded practice questions
- Freeno payment, ever
The Computer Vision Engineer roadmap
Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.
Phase 1Computer vision foundations
Image data and preprocessing
Pixels, color spaces, and resizing are only half the job.
CNNs and vision transformers
CNNs bake in locality and translation equivariance, making them sample-efficient; plain ViTs drop those biases for global self-attention and shine mainly with large-scale pretraining or heavy…
Evaluation metrics (IoU, mAP)
IoU measures box/mask overlap; mAP averages precision across recall and IoU thresholds.
Phase 2Computer vision core tasks
Image classification
Classification assigns one (or multi-) label per image.
Object detection
Detectors output boxes with class and confidence.
Segmentation
Semantic segmentation labels every pixel by class; instance segmentation gives each object its own mask; panoptic unifies both.
Phase 3Data and training
Data augmentation
Augmentation expands effective data and encodes the invariances you want, but every transform must preserve label semantics.
Transfer learning
Reusing pretrained backbones beats training from scratch on small datasets.
Labeling and imbalance
Under extreme class imbalance, uniform random labeling spends most of the budget re-confirming the easy majority.
Phase 4Multimodal and generative
Vision-language models
CLIP-style models embed images and text into a shared space, enabling zero-shot classification by matching images to text prompts.
Image generation (diffusion)
Diffusion models generate images by iteratively denoising, and can be fine-tuned on domain data.
OCR and document AI
Turning scanned documents into structured data is reliable when decomposed: deskew the page, detect layout and tables, OCR the text, then parse fields, validating at each stage.
Phase 5Computer vision in production
Model optimization for vision
Quantization, pruning, and distillation shrink models and cut latency for tight budgets.
Deployment and edge
Choosing cloud vs edge inference is a requirements problem.
Monitoring and drift
Deployed vision models degrade when inputs drift (new lighting, cameras, or product mix) even with unchanged code and weights.
Reading for this path
The primary sources behind the topics above, all free to read.
- scikit-image: image data types and rescaling
- PyTorch: transforming and augmenting images
- Dosovitskiy et al., An Image is Worth 16x16 Words (ViT)
- PyTorch: models and pretrained weights
- Ultralytics: performance metrics deep dive (mAP, IoU)
- COCO: detection evaluation protocol
- PyTorch: transfer learning / classification tutorial
- scikit-learn: classification metrics guide
- Ultralytics YOLO documentation
- Redmon et al., You Only Look Once (YOLO)
- He et al., Mask R-CNN
- PyTorch: object detection & instance segmentation tutorial
- PyTorch: illustration of transforms / augmentation
- scikit-image: geometric transformations
Coming from another job?
Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.
Other AI career paths
Context Engineer
Context windows, memory systems, retrieval strategy, and token budgeting. Get the right information in front of the model every time.
Conversational AI Designer
Dialog flows, persona design, voice and chat UX, and escalation paths. Craft conversations people actually want to have.
Data Scientist
Experiment design, statistics, causal inference, and modeling. Turn data into decisions and explain why.
Edge AI Engineer
On-device models, quantization for mobile and embedded, and offline inference. Run AI where the cloud can't reach.
Forward-Deployed Engineer
Workflow discovery, ambiguous requirements, agent-solution scoping, and deployment planning. Turn vague customer asks into scoped, shippable agent solutions.
Generative Media Engineer
Image, video, audio, and 3D generation - pipelines, controllability, and creative tooling. Build the systems behind generative media.
Start the Computer Vision Engineer path for free
The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.
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