Computer Vision Engineer

What does a Computer Vision Engineer do?

Computer Vision Engineers build systems that interpret visual data from images and videos. You turn pixels into decisions: detect defects on a factory line, understand road scenes for ADAS, power visual search, or anonymize sensitive faces.

Typical deliverables include:

• Production-ready models (classification, detection, segmentation, keypoint, OCR)
• Reusable data pipelines for labeling, preprocessing, and augmentation
• Evaluation reports with metrics (accuracy, IoU, mAP, latency, throughput)
• Deployment artifacts (ONNX/TensorRT/TF Lite models, Docker images, REST/gRPC services)
• Monitoring dashboards for drift, quality, and performance

Day-to-day responsibilities

• Define problem scope, success metrics, and data requirements with product and domain experts
• Collect and annotate data; maintain labeling guidelines and quality checks
• Build preprocessing and augmentation pipelines
• Train and iterate on model architectures; perform error analysis
• Package and deploy models; optimize for latency, memory, and cost
• Monitor performance in production; respond to drift and edge cases
• Document experiments, decisions, and model cards

Where you can work

• Autonomous systems and robotics (perception stacks)
• Manufacturing (defect detection, counting, safety)
• Healthcare (medical imaging, triage, segmentation)
• Retail and e-commerce (visual search, shelf analytics)
• Security and privacy (anonymization, redaction)
• Media and entertainment (AR filters, content moderation)
• Mapping and satellites (remote sensing, change detection)

Hiring expectations by level

Salary ranges

Approximate total compensation (USD):

• Junior: $80k–$130k
• Mid-level: $120k–$180k
• Senior/Staff: $170k–$260k+

Varies by country/company; treat as rough ranges.

Who this is for

• Engineers who enjoy applied math, optimization, and building production systems
• Problem-solvers who iterate with data and love debugging edge cases
• People comfortable with trade-offs: speed vs accuracy vs cost

Prerequisites

• Python fundamentals and comfort with NumPy
• Basic linear algebra, probability, and calculus intuition
• Familiarity with Git and Linux basics

Skill map

• Computer Vision Foundations: images, color spaces, convolutions, classic CV vs deep learning
• Data Collection And Annotation: sampling strategy, labeling tools, guidelines, quality control
• Image Preprocessing And Augmentation: normalization, resizing, geometric/photometric transforms
• Vision Model Architectures: CNNs, ResNets, UNet, YOLO/DETR, ViT
• Training And Optimization: loss functions, schedulers, regularization, mixed precision
• Feature Extraction And Embeddings: SIFT/ORB, deep embeddings, retrieval
• Evaluation And Error Analysis: IoU, mAP, PR curves, slicing and cohort analysis
• Deployment And Model Serving: ONNX, TensorRT, TF Lite, REST/gRPC, batching
• Video And Streaming Vision: tracking, temporal models, buffering, latency
• MLOps For Vision Systems: data/versioning, CI/CD, monitoring, retraining
• Safety And Compliance For Vision: privacy, bias, redaction, model cards

Learning path

1. Foundations first: OpenCV, tensors, basic CNNs. Mini task: reproduce a simple classifier baseline.
2. Data pipeline: Collect, annotate, and augment a small dataset. Write labeling guidelines.
3. Architectures: Try ResNet vs EfficientNet; UNet for segmentation; YOLO/DETR for detection.
4. Train and tune: Learn schedulers, early stopping, mixed precision; profile bottlenecks.
5. Evaluate deeply: Slice by conditions; analyze false positives/negatives; adjust thresholds.
6. Deploy: Export to ONNX; optimize (quantization, fusion); serve with REST/gRPC.
7. Monitor and iterate: Track drift and latency; set retraining triggers.

Practical projects

• Defect detection on parts
  Outcome: Binary detector with < 30 ms inference on 224×224 images; a clear procedure for labeling borderline cases.
  Mini task: Create a confusion matrix by defect subtype.
• Road-sign detection
  Outcome: YOLO/DETR model with mAP@0.5 > 0.85; threshold policy per class.
  Mini task: Compare NMS vs class-wise NMS impact.
• Semantic segmentation for crops or cells
  Outcome: UNet with IoU > 0.75; augmentations that preserve labels.
  Mini task: Visualize 20 random masks overlaid on inputs for QC.
• Face anonymization pipeline
  Outcome: Detector + blurring/redaction service; evidence of recall > 0.98 on faces.
  Mini task: Measure latency distribution p50/p95.
• Image similarity search
  Outcome: Embedding model + index (FAISS-like) with top-5 precision > 0.8.
  Mini task: Evaluate retrieval by category and lighting condition.

Interview preparation checklist

• Explain convolution, padding/stride, and receptive fields clearly
• Compare detectors (YOLO vs Faster R-CNN vs DETR) and when to choose each
• Compute IoU, precision/recall, F1, and mAP; read PR curves
• Walk through a rigorous error analysis workflow
• Discuss deployment constraints and optimization strategies
• Risk and safety: privacy, bias, consent, and safe failure behavior
• Whiteboard a minimal serving architecture with monitoring

Common mistakes (and how to avoid them)

• Training-test leakage via augmentations or improper splits -> Use stratified, scene-aware splitting
• Optimizing for accuracy when class imbalance demands recall/F1 -> Align metrics with real risk
• Ignoring data quality -> Write labeling guidelines and run regular QC reviews
• Deploying without monitoring -> Track drift, latency, and class-wise metrics from day one
• Overfitting to benchmarks -> Validate on real, unseen conditions and edge cases

Skills to master

Scroll through the Skills section on this page and pick one to start. Each skill includes why it matters, difficulty, and time estimates.

Next steps

• Take the fit test to gauge your match
• Pick a skill to start and complete one mini project
• Return for the exam to validate your readiness

Pick a skill to start.