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Hiring machine learning engineers
Your guide to finding and hiring the right person for your organization
Top hard skills interview questions for hiring machine learning engineers
How do you prevent overfitting in a machine learning model?
Why this matters:
Overfitting is a common issue in machine learning. This question will allow the candidate to showcase their technical skills and unique approach to problem-solving. The best answers will address common setbacks in the overfitting correction process and how to overcome them.
What to listen for:
- Deep familiarity with the causes and consequences of overfitting in machine learning models
- Detailed explanations of different techniques to correct overfitting
- Past examples where the candidate successfully corrected an overfitting issue
What steps do you take to ensure that a machine learning model will scale?
Why this matters:
In machine learning, scalability is essential to efficiently process large quantities of data. This question starts a conversation around the importance of scalability, and allows the candidate to demonstrate their knowledge of machine learning fundamentals.
What to listen for:
- Specific techniques for writing clean and scalable code in depth
- Experience building scalable machine learning models
- Business benefits that scalable machine learning models can provide, such as deep learning
In your opinion, what is the most valuable application for machine learning in our sector?
Why this matters:
This question offers the candidate a chance to showcase their familiarity with your organization’s work and expand on common and overlooked use cases for machine learning. The best candidates will have a good grasp of general business problems in your sector and a clear understanding of how machine learning can help solve them.
What to listen for:
- Familiarity with your organization’s products or services and customer base
- Ability to apply machine learning models to real-world scenarios
- Creative, feasible solutions
Top behavioral interview questions for hiring machine learning engineers
In your most recent role, how did you and your teammates ensure that projects aligned with broader business goals?
Why this matters:
In tech, generating business value is not always straightforward. To avoid wasting time and resources on fruitless projects, your machine learning team must structure its work around a value proposition that solves a relevant problem for your organization.
What to listen for:
- Clear understanding of machine learning’s role in supporting business goals
- Experience deploying machine learning models with practical applications
- Instinct to gather input from other departments
Tell me about the most challenging project you’ve worked on. How did you and your team collaborate to overcome difficulties and devise a solution?
Why this matters:
Machine learning is a highly collaborative discipline. Your team needs a machine learning engineer with impeccable teamwork and communication skills. The best candidates use respectful language when referring to their peers and highlight each member’s contribution to the project.
What to listen for:
- Strong communication and interpersonal skills
- Experience working in team settings
- Solid understanding of the collaborative nature of machine learning and technology
In your past role, how did you test machine learning models to ensure they performed as intended?
Why this matters:
In addition to understanding traditional software testing techniques, machine learning engineers must be adept at building and running tests for models. Pay attention to which frameworks and best practices the candidate employed to ensure accurate model testing. Top candidates will convey an uncompromising commitment to producing high-performing machine learning models.
What to listen for:
- Emphasis on running pretraining tests to catch bugs early
- Familiarity with common difficulties and setbacks of the testing process and how to overcome them
- Processes for testing both traditional software and machine learning software
Top soft skills interview questions for hiring machine learning engineers
How do you balance meeting deadlines with building high-performing machine learning models?
Why this matters:
Since machine learning technology is still in its infancy, projects are notoriously time-consuming and difficult. Candidates should be well aware of the iterative nature of machine learning projects and be able to list best practices that speed up development and ensure a high-performing final product. Look for answers that convey a can-do attitude and enthusiasm for problem-solving.
What to listen for:
- Use of structured data for building functional machine learning models
- Best practices in time management and efficiency
- Acknowledgment of common difficulties in building and deploying machine learning models
Who should be held accountable if a machine learning model makes a mistake?
Why this matters:
The nature of machine learning technology poses some difficult ethical questions for which there are yet to be clear answers. This question will test your candidate’s ability as a professional in the machine learning field to have an in-depth discussion on the most pressing ethical issues surrounding these technologies. The best answers will mention viable solutions for using machine learning technology responsibly and humanely.
What to listen for:
- Sense of responsibility and accountability
- Familiarity with common ethical issues in machine learning
- Prioritization of staying abreast of upcoming solutions to machine learning’s ethical issues
How do you stay up to date on the latest developments in the machine learning field?
Why this matters:
As an emerging technology, machine learning is constantly evolving. To remain competitive as professionals, your candidates must be committed to continuous education and closely follow industry news. Pay attention to the tone and attitude of your interviewees when answering this question. The best candidates will convey genuine excitement and enthusiasm about the field.
What to listen for:
- Well-known and authoritative sources of information for industry news and developments
- Courses or certificates they’ve completed in the past
- Demonstrated commitment to continuous learning
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