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Junior Machine Learning Engineer Interview Questions

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8,205 junior machine learning engineer interview questions shared by candidates

I was given an engineering interview roadmap that will be outlined below verbatim: Recruiter phone screen (30 mins)-This is a chance for your recruiter to learn about your background and interests, what you are passionate about, and the impact you want to make in your next move. Technical phone screen (60 mins) - You will meet with an Affirm Engineer to further assess your technical skills through a live pair-programming session. Onsite - Practical coding (60 mins) - Like the technical phone screen, the practical coding session is intended to give us an opportunity to assess your coding ability, and other themes such as readability and decomposition. You should not expect to be doing heavy algorithmic questions. Instead, the questions we will work on together will be cross-cutting across a backend focus while still evaluating your general technical skill. Platform-specific questions may be asked depending on the context of the role. Onsite - Systems design (60 mins) -We will work through various technical problems that can be solved in a myriad of ways by defining the architecture, modules, interfaces and data that comprise a system. As part of the exercise, you may be asked to whiteboard some simple code or pseudocode to help illustrate your design Onsite - Hiring manager (60 mins) - this interview with the hiring manager will explore your background and skills. This interview will also give you the opportunity to ask questions about the team, projects you would be working on, and learn more about Affirm. Additionally, expect questions around contributing to Affirm’s culture of inclusion, working effectively with a global team and for prospective managers, your ability to ensure all team members are seen and heard equitably. Lastly, be prepared to share your experience with mentoring or leading all or part of a recent project. Offer - Congratulations, you made it! Your recruiter will schedule time with you to discuss offer details and talk through next steps.
avatar

Machine Learning Engineer

Interviewed at Affirm

4★
Feb 3, 2025

I was given an engineering interview roadmap that will be outlined below verbatim: Recruiter phone screen (30 mins)-This is a chance for your recruiter to learn about your background and interests, what you are passionate about, and the impact you want to make in your next move. Technical phone screen (60 mins) - You will meet with an Affirm Engineer to further assess your technical skills through a live pair-programming session. Onsite - Practical coding (60 mins) - Like the technical phone screen, the practical coding session is intended to give us an opportunity to assess your coding ability, and other themes such as readability and decomposition. You should not expect to be doing heavy algorithmic questions. Instead, the questions we will work on together will be cross-cutting across a backend focus while still evaluating your general technical skill. Platform-specific questions may be asked depending on the context of the role. Onsite - Systems design (60 mins) -We will work through various technical problems that can be solved in a myriad of ways by defining the architecture, modules, interfaces and data that comprise a system. As part of the exercise, you may be asked to whiteboard some simple code or pseudocode to help illustrate your design Onsite - Hiring manager (60 mins) - this interview with the hiring manager will explore your background and skills. This interview will also give you the opportunity to ask questions about the team, projects you would be working on, and learn more about Affirm. Additionally, expect questions around contributing to Affirm’s culture of inclusion, working effectively with a global team and for prospective managers, your ability to ensure all team members are seen and heard equitably. Lastly, be prepared to share your experience with mentoring or leading all or part of a recent project. Offer - Congratulations, you made it! Your recruiter will schedule time with you to discuss offer details and talk through next steps.

You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.
avatar

Machine Learning Scientist

Interviewed at Varian Medical Systems

4★
Dec 28, 2021

You will be asked a wide range of ML-related questions (ML theory, PyTorch, CNNs, etc.). You will also be asked to code towards the end of the 1 hour session (Leetcode medium). Most of these questions have well-defined answers (e.g., how do you disable gradient computation in PyTorch) while others are more open-ended (e.g., how would you use unlabeled data to boost the performance of your supervised tasks). My major complaints are with these open-ended questions. The interviewer had specific answers in mind and would not understand/accept alternative approaches. The depth of the interviewer's ML knowledge is also questionable as the interviewer did not understand how pretrained networks can be used as feature extractors. The interviewer also asked about variational auto-encoder without knowing the underlying probabilistic formulation. Overall, a negative experience.

Example Questions: Q: How would you visualize and present a particularly complex data set? Q: How would deal with a high dimensional data set? Q: What ML techniques are you familiar with and what are the tradeoffs between them?
avatar

Machine Learning Engineer

Interviewed at Whip Media

3.5★
Aug 8, 2020

Example Questions: Q: How would you visualize and present a particularly complex data set? Q: How would deal with a high dimensional data set? Q: What ML techniques are you familiar with and what are the tradeoffs between them?

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