Recruiter's Glossary

Examples: Transfer Learning • TensorFlow • BERT

Computer Vision

Term from Artificial Intelligence industry explained for recruiters

Computer Vision is a technology that helps computers understand and work with images and videos, similar to how humans use their eyes to see and understand the world. It's a key part of artificial intelligence that allows machines to perform tasks like recognizing faces in photos, helping self-driving cars identify road signs, or enabling quality control systems to spot defects in manufacturing. Think of it as giving computers the ability to "see" and make decisions based on visual information. This technology is becoming increasingly important across many industries, from healthcare (analyzing medical images) to retail (powering checkout-free stores) to security (surveillance systems).

Examples in Resumes

Developed Computer Vision algorithms to automate quality control in manufacturing

Implemented Computer Vision and Machine Vision systems for facial recognition security

Led team of engineers in creating Computer Vision solutions for autonomous vehicles

Applied CV and Computer Vision technology to medical image analysis

Typical job title: "Computer Vision Engineers"

Also try searching for:

Computer Vision Engineer Machine Learning Engineer AI Engineer Vision Systems Engineer Deep Learning Engineer AI Developer Computer Vision Researcher

Where to Find Computer Vision Engineers

Online Communities

Job Boards

Professional Networks

Events & Conferences

Example Interview Questions

Senior Level Questions

Q: How would you approach building a system to detect product defects in a manufacturing line?

Expected Answer: A senior candidate should explain the process of collecting and labeling image data, choosing appropriate algorithms, considering real-time processing requirements, and implementing quality control measures. They should also discuss handling various lighting conditions and different types of defects.

Q: What experience do you have with scaling computer vision solutions in production environments?

Expected Answer: Should discuss experience with handling large amounts of visual data, optimizing processing speed, managing computing resources, and ensuring system reliability. Should mention real-world challenges and solutions they've implemented.

Mid Level Questions

Q: Can you explain the difference between image classification and object detection?

Expected Answer: Should be able to explain in simple terms that image classification identifies what's in an image overall, while object detection finds and labels specific objects within an image, giving their location and boundaries.

Q: What methods would you use to improve the accuracy of a computer vision model?

Expected Answer: Should discuss collecting more diverse training data, data augmentation techniques, adjusting model parameters, and validating results. Should demonstrate understanding of common problems and solutions.

Junior Level Questions

Q: What are the basic steps in processing an image for computer vision?

Expected Answer: Should be able to explain basic concepts like loading an image, converting colors, reducing noise, and extracting features in simple terms. Understanding of basic image manipulation is important.

Q: How would you approach a simple face detection task?

Expected Answer: Should demonstrate understanding of using pre-built libraries and basic concepts of feature detection. Should be able to explain the process in simple, logical steps.