Recruiter's Glossary

Examples: K-means • Named Entity Recognition • Supervised Learning

Neural Networks

Term from Artificial Intelligence industry explained for recruiters

Neural Networks are computer systems designed to learn and make decisions in ways that mimic the human brain. Think of them as digital brains that can learn from examples to perform tasks like recognizing images, understanding text, or making predictions. When you see this term in a resume, it usually means the candidate has experience building or working with these learning systems. They're a key part of artificial intelligence and machine learning. Other common names for this technology include "deep learning," "artificial neural networks," or "ANN." Companies use neural networks for things like customer service chatbots, product recommendations, or detecting unusual patterns in data.

Examples in Resumes

Developed Neural Network models for customer behavior prediction

Implemented Neural Networks and Deep Learning systems for image recognition

Optimized Artificial Neural Network performance for real-time data processing

Typical job title: "Neural Network Engineers"

Also try searching for:

AI Engineer Machine Learning Engineer Deep Learning Engineer AI Developer Machine Learning Scientist Neural Network Developer AI Research Engineer

Where to Find Neural Network Engineers

Online Communities

Job Boards

Events & Conferences

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you explain the business value of neural networks to non-technical stakeholders?

Expected Answer: A senior candidate should be able to clearly explain how neural networks can solve real business problems and provide ROI through examples like customer predictions, process automation, or cost reduction, without using technical jargon.

Q: What's your approach to choosing between different types of neural networks for a project?

Expected Answer: Should demonstrate decision-making ability based on project requirements, available data, computing resources, and business constraints, while explaining trade-offs in simple terms.

Mid Level Questions

Q: Can you describe a challenging neural network project you worked on and how you solved the problems you encountered?

Expected Answer: Should be able to walk through a real project example, explaining the challenges faced and solutions implemented in clear, business-focused terms.

Q: How do you ensure the reliability and accuracy of neural network models?

Expected Answer: Should explain their testing and validation approaches, how they measure success, and methods for ensuring the model performs well in real-world conditions.

Junior Level Questions

Q: What basic types of problems can neural networks solve?

Expected Answer: Should be able to explain common applications like image recognition, text analysis, or prediction tasks using simple, non-technical language and real-world examples.

Q: How do you prepare data for use in neural networks?

Expected Answer: Should demonstrate understanding of basic data preparation steps, explaining why clean and properly formatted data is important for good results.