9 Common interview questions for AI jobs

Artificial intelligence (AI) is a rapidly growing field, and as a result, the job market for AI professionals is expanding. AI job interviews can be particularly challenging because of the technical nature of the field. However, technical expertise is not the only factor that interviewers consider. Non-technical candidates who can demonstrate an understanding of AI concepts and an eagerness to learn are also valued.

Technical candidates should be prepared to answer questions that test their knowledge of machine learning algorithms, tools and frameworks. They may be asked to provide detailed explanations of their past projects and the technical solutions they used to overcome challenges. Additionally, they should be prepared to answer questions about data preprocessing, model evaluation and their experience with AI-related tools and frameworks.

Related: 5 natural language processing (NLP) libraries to use

Non-technical candidates should focus on their understanding of the transformative potential of AI and their eagerness to learn more about the field. They should be able to explain the importance of data preprocessing and cleaning and provide an understanding of how machine learning algorithms work. Additionally, they should be prepared to discuss their ability to collaborate and communicate with team members and their methods of staying up-to-date with the latest developments in AI.

Here are nine common interview questions for AI jobs. While these are common interview questions for AI jobs, it’s important to keep in mind that every job and company is unique. The best answers to these questions will depend on the specific context of the role and the organization you are applying to.

Use these questions as a starting point for your interview preparation, but don’t be afraid to tailor your responses to fit the specific job requirements and culture of the company you are interviewing with. Remember that the goal of the interview is to demonstrate your skills and experience, as well as your ability to think critically and creatively, so be prepared to provide thoughtful and nuanced responses to each question.

1. What motivated you to pursue a career in AI?

This question is aimed at understanding a job seeker’s motivation and interest in pursuing a career in AI. It is an opportunity to showcase one’s passion and how it aligns with the job they are applying for. A candidate’s answer should highlight any experience or training they may have had that sparked their interest in AI, as well as any specific skills or interests they have in the field. 

Recipe to getting a job in data science in 6 months

– Learn Python & SQL
– Brush up on stats & linear algebra
– Implement key ML algorithms using Kaggle data in notebooks
– Use real-world data, build machine learning models
– Practice interview questions

Get job 🙂

— Bindu Reddy (@bindureddy) March 3, 2021

Technical candidates can highlight their interest in the mathematical and statistical foundations of machine learning, while non-technical candidates can focus on the transformative potential of AI and their desire to learn more about the field.

2. What experience do you have with AI-related tools and frameworks?

This question is aimed at assessing a candidate’s technical knowledge and experience with AI-related tools and frameworks. Their answer should highlight any experience they have had working with specific tools and frameworks, such as TensorFlow, PyTorch or scikit-learn. 

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ML: NumPy/Scipy, Pandas, SkLearn
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Technical candidates can provide specific examples of tools and frameworks they have worked with, while non-technical candidates can highlight their willingness to learn and adapt to new technologies.

3. Can you describe a machine learning project you worked on?

This question is designed to assess the candidate’s experience and understanding of machine learning projects. The interviewer is interested in hearing about a machine learning project that the candidate has worked on in the past. The candidate’s response should be structured to describe the project from start to finish, including the problem that was being solved, the data used, the approach taken, the models developed and the results achieved.

The candidate should use technical terms and concepts in their answer but also explain them in a way that is easy to understand for non-technical interviewers. The interviewer wants to gauge the candidate’s level of understanding and experience with machine learning projects, so the candidate should be prepared to provide details and answer follow-up questions if necessary.

Technical candidates can provide a detailed explanation of the project,…