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Welcome to Exponent's ML Engineer Interview Course!

With AI and ML-powered technologies increasing rapidly, so is the demand for machine learning (ML) engineers. You might be confused about the best approach to preparing for your interview, and you’re not alone. We partnered with ML experts from FAANG companies and top startups to create this comprehensive course.

After coaching hundreds of clients, we’ve concluded that nothing works better than practice. That's why the Exponent membership includes:

Goals of this course

By the end of the course, you should be able to:

  • Understand what's evaluated in the ML engineer interview loop.
  • Discuss complex technical decisions in ML system design interviews.
  • Answer questions about fundamental ML concepts and techniques with confidence.
  • Implement advanced ML coding solutions.
  • Create a rich story bank based on your past experiences for behavioral interviews.

How to use this course

ML engineer interview questions generally fall into the following categories:

  • ML system design. These questions ask you to design an ML system from end-to-end.
  • ML coding. These questions ask you about your understanding of an ML framework (e.g. TensorFlow, PyTorch) and a core ML concept relevant to the team's sub-field (e.g. transformers, convolutional nets).
  • ML concepts. These questions test you on fundamental ML concepts with an ML engineer or scientist.
  • Behavioral. These questions assess whether your skills and working style align with those of the team.

If you have a specific way you prefer to learn, follow that method as you go through the course. If you’d like more direction, we recommend reading the conceptual lessons, and then watching their respective mock interviews. Once you’ve understood the general types of questions and frameworks, test yourself on a question in each category.

Lastly, once you’ve completed all the lessons and questions, there’s even more practice in our interview question database. We select the top answers to give detailed feedback and analysis.

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