Recruiter's Glossary

Examples: Grid Search • Regression • Feature Engineering

Computer Vision

Term from Data Science industry explained for recruiters

Computer Vision is a field where computers are taught to understand and analyze images and videos, similar to how human eyes and brain work together. It's like giving computers the ability to 'see' and make sense of visual information. This technology is used in many everyday applications, from facial recognition on smartphones to quality control in manufacturing, or even in self-driving cars. When someone mentions Computer Vision in their resume, they typically work on creating systems that can automatically detect objects, recognize faces, read text from images, or track movement in videos.

Examples in Resumes

Developed Computer Vision algorithm to automate quality control in manufacturing

Implemented Computer Vision and CV systems for real-time face detection

Led team in creating Computer Vision solutions for retail customer tracking

Typical job title: "Computer Vision Engineers"

Also try searching for:

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

Where to Find Computer Vision Engineers

Online Communities

Job Boards

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you approach building a system that needs to work in varying lighting conditions?

Expected Answer: A senior candidate should discuss different strategies for handling lighting changes, mention real-world project examples, and explain how they would test and validate the solution's reliability.

Q: How would you lead a computer vision project from start to finish?

Expected Answer: Should demonstrate project management experience, discuss gathering requirements, choosing appropriate technologies, managing team members, and ensuring quality throughout development.

Mid Level Questions

Q: What methods would you use to detect objects in images?

Expected Answer: Should be able to explain basic object detection concepts in simple terms and discuss different approaches they've used in real projects.

Q: How do you ensure your computer vision system performs well with different types of images?

Expected Answer: Should discuss testing with various image types, handling edge cases, and methods to improve system reliability.

Junior Level Questions

Q: Can you explain what image preprocessing is?

Expected Answer: Should be able to explain basic concepts of preparing images for analysis, like resizing, normalizing brightness, or removing noise, in simple terms.

Q: What basic tools have you used for computer vision projects?

Expected Answer: Should be familiar with common software libraries and basic image processing concepts, even if experience is mainly from academic projects.