Competitive candidates understand how to speak confidently about their technical and transferable skills in ways that sell their professional value to employers. Not there yet? Let us help.
This guide provides a list of sample interview questions and tips on how to build your responses to common interview questions in AI.
Interview questions by category
We have categorized the interview questions into three sections. Feel free to read straight through or jump to the sections that interest you most.
Tell me about yourself.
Give a snapshot of your work history. Think about your past (previous experience and education), present (your current job and how it’s preparing you for this role), and future (why you want the job you’re interviewing for). Your snapshot should show the interviewer how your experiences equipped you with the skills to succeed at their company, and why you are interested in the specific role.
What are your greatest strengths and weaknesses as a professional?
Consider the job description and the required skills in the posting, and align your answer with a skill you’ve mastered. Discuss your areas of expertise and how they will benefit the organization, team, or position. For weaknesses, discuss a skill that you are actively working to improve. Explain the steps that you have already taken to improve this skill, and the success that you have already achieved.
Why do you want this job?
Demonstrate that you’ve researched the company, and express what you like about it and why your skills would be a good fit for the job. Explain how the role will contribute to your career progression and what you can contribute to the team. Be specific and express enthusiasm about potentially working for the company.
Where do you see yourself in five years?
This question is designed to probe your career goals and vision. Tell your interviewer about your career aspirations and ambitions. Express your desire to succeed and explain how this job will contribute to your progress. This is an opportunity to show that you are goal-oriented and that you have a plan to achieve those goals.
What are your salary expectations?
Prepare thoroughly for this question. Research salary ranges for this particular job, this industry, and your geographic area. Use websites like Payscale.com or Salary.com. The interviewer wants to know how you value yourself. It is recommended that you avoid speaking about salary until an offer is made. If you must speak about salary expectations, ask the interviewer how they value the role before stating your expectations. Use the salary given by the interviewer as leverage to negotiate a favorable salary. Aim high, but within the range you’ve researched, and be prepared to provide examples of why you’re worth the salary that you cite.
Pro tip:
Salary negotiation is a hot topic these days. Check out our Salary negotiation guide to learn more about earning what you are worth.
What is the difference between supervised and unsupervised learning?
Describe the difference, and be ready to explain with examples. For supervised learning, an example may be classifying emails as spam or non-spam. For unsupervised learning, an example could be clustering customer data to identify market segments.
Discuss the bias-variance tradeoff in machine learning and its implications?
Balancing bias and variance is crucial to achieve optimal model performance. Include examples of the dilemma in machine learning between models that are too simple (high bias) or too complex (high variance).
How do you evaluate the performance of a machine learning model? What evaluation metrics would you use?
Employers ask this question in order to understand your ability to use metrics such as accuracy, precision, recall, or F1 score. Use the STAR method to craft a concise yet thorough description of a time that you got an unexpected result. Explain the actions you took in response to the data. You can learn about the STAR Method in our Behavioral Interview Prep Guide.
What is the difference between machine learning and AI?
Remember that machine learning is a tool within AI that enables computers to learn and improve performance without explicit programming. AI is a broad field, while machine learning is a subset of AI. Provide a clear example that demonstrates your understanding of the two concepts.
Describe how you would handle missing or incomplete data in a machine learning project.
This question calls out a common issue in the industry. Employers want to know how you will handle this situation when it occurs. Name and describe some of the strategies that one can use when there is missing data. Consider using the STAR method to describe a time when you successfully used one of these strategies. (Learn more about the STAR method in our Behavioral interview prep guide.)
How would you approach a situation where a deployed AI model is not performing as expected?
This question can be tricky if you are a career starter or career switcher. You may feel a desire to say, “I don’t know” or “I haven’t been in that situation yet.” Use this as an opportunity to discuss a challenge you had in a project or consider a scenario that focuses on possible solutions. There is no correct answer to a question like this, as employers want to understand your thought process and approach.
Technical screenings:
You will find that some employers include technical screenings as a part of the hiring process. Technical screenings are an opportunity for you to demonstrate your technical skills in the form of an assessment, live interview, or take-home project. Learn more about technical screenings in our Technical screening guide.
Interview questions for entry-level professionals
Tell me about a time that you got unexpected results.
Your response to this question should show that you are analytical and display problem-solving skills. Use the STAR method to craft a concise, yet thorough description of a time that you got an unexpected result, and explain the actions that you took in response.
How do you stay abreast of industry trends?
This is an excellent opportunity to show that you are passionate and willing to put forth the extra effort to stay current on the latest news and trends in the industry. Cite three to four ways you track the industry and how each process or tool benefits you.
Describe a machine learning algorithm you are familiar with and its practical applications.
This is the time to let your strengths shine through. Walk through your algorithm of choice in an organized, clear response that will demonstrate your knowledge and reasoning.
Interview questions for leadership professionals
Why do you want to be a leader in our company?
Your response gives the interviewer a sense of how you’ve conceptualized this leadership role and if you’re ready for the responsibilities. They may also want to assess how well your professional values align with the company values. Discuss what you enjoy about being a leader and/or experiences that have prepared you for leadership.
How would you go about measuring the performance of our company?
Your response to this question should show that you know how to develop and manage data, and that you understand the needs of the company. Consider including a story about a relevant project that you have completed.
Expert advice
“It’s important for the employer to have an understanding of how you approach your work – how you work with others, how you approach and manage projects and how you solve problems. If you don’t give them tangible examples and/or evidence of doing this in a positive and helpful way, they don’t know enough about you to make an informed decision.”
– Deanna P., career expert at edX
How do you explain your results and processes to non-technical audiences?
Questions asking candidates to describe a technical topic to a non-technical person are designed to test communication skills. Your answer should show that you are able to effectively discuss your technical projects with a wide range of stakeholders. Consider using the STAR Method to discuss a time that you collaborated with non-technical stakeholders.
Interview questions for career-switching professionals
Why are you leaving your current job or changing career paths?
This can be a tricky question; always remain positive. When discussing your current company or environment, give a truthful reason as to why you’re leaving, but don’t bring negativity into the discussion. Focus your answer on developing and expanding your career into the AI industry. Share what you’ve learned about this potential new employer and how it’s a good fit for your goals, strengths, and experience.
In your opinion, what makes a machine learning engineer great?
We recommend considering both technical skills and soft skills. Your response should provide a clear example of your leadership and skills.
What is your process of training a machine learning model?
Employers want to ensure that candidates know the approach of the process. This may include dividing the data into training and validation sets, initializing the model, calculating loss or error, and updating the model. You should value data cleaning as a part of the data analysis process. Use the STAR method to concisely share a time that you used your process.
