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Machine Learning Engineer Interviews

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Machine Learning Engineer Interview Questions

Companies rely on machine learning engineers to help design and improve the systems that allow their software to improve on its own, rather than being specifically programmed. During the interview process, be prepared to be tested heavily on both computer science and data science knowledge with an emphasis on recognizing patterns and trends. A bachelor's degree in computer science or a related field will be required.

Top Machine Learning Engineer Interview Questions & How to Answer

Question 1

Question #1: What are the most important algorithms, programming terms, and theories to understand as a machine learning engineer?

How to answer
How to answer: Be prepared to talk about things like Type I and Type II errors, supervised and unsupervised machine learning, ROC curves, and other key parts of machine learning. Employers want to know you have a strong knowledge of the technical aspects of the job position.
Question 2

Question #2: How would you explain machine learning to someone who doesn't understand it?

How to answer
How to answer: Sometimes machine learning engineers have to work with people who aren't familiar with the technical aspects of the job. Use this interview question as an opportunity to show your strong knowledge of the position and your communication abilities.
Question 3

Question #3: How do you stay up to date with the latest news and trends in machine learning?

How to answer
How to answer: By talking about how you're up to date with the latest news and trends in machine learning, you can show an employer that you're engaged in the industry, a skilled researcher, and self-motivated.

8,210 machine learning engineer interview questions shared by candidates

1. Call with the Recruiter: The questions were general and non-technical, such as "Tell me about yourself and your experience" and "Why are you interested in Ninety?" Typical questions that you'd expect from a non-technical recruiter. 2. Technical Interview: First part: I was asked to explain a sentiment analysis implementation, walking through the code line by line while thinking out loud. This included discussing how the code works, identifying the hyper-parameters, and suggesting ways to tune and improve performance. Second part: This focused on a broad range of machine learning and data science topics. Some key questions included: Explain Naive Bayes and Bayes' Theorem. How does it work? What is the Transformer architecture? Can you explain each component in detail? How is feature engineering done in Computer Vision tasks? What is the ResNet architecture, and how does it work? The interview covered both breadth and depth across ML/DL topics. Some LLM questions, involving RAG, etc. The other two interview: project that you're proud of? the challenges you face doing a project and how did you resolve it? followed by many more questions about the current project that they had and the challenges and asked me how would I approach them and my resolutions? ...
avatar

Sr. Machine Learning Engineer

Interviewed at Ninety

3.2★
Sep 5, 2024

1. Call with the Recruiter: The questions were general and non-technical, such as "Tell me about yourself and your experience" and "Why are you interested in Ninety?" Typical questions that you'd expect from a non-technical recruiter. 2. Technical Interview: First part: I was asked to explain a sentiment analysis implementation, walking through the code line by line while thinking out loud. This included discussing how the code works, identifying the hyper-parameters, and suggesting ways to tune and improve performance. Second part: This focused on a broad range of machine learning and data science topics. Some key questions included: Explain Naive Bayes and Bayes' Theorem. How does it work? What is the Transformer architecture? Can you explain each component in detail? How is feature engineering done in Computer Vision tasks? What is the ResNet architecture, and how does it work? The interview covered both breadth and depth across ML/DL topics. Some LLM questions, involving RAG, etc. The other two interview: project that you're proud of? the challenges you face doing a project and how did you resolve it? followed by many more questions about the current project that they had and the challenges and asked me how would I approach them and my resolutions? ...

SQL: joins, window functions python: new column assignment, string and list manipulations How does XGBoost work? step by step process with example How to represent categorical column with high cardinality How can embeddings be generated? How encoder-decoder might help in this How to find presence of multicollinearity in data? What is chi square test of independence? How to handle imbalanced classification? Why is PR auc better suited to such cases then ROC auc? How to handle large no. of classes in multiclass classification? What is negative sampling and how does it help in such scenarios?
avatar

Machine Learning Specialist

Interviewed at PayPal

3.5★
Jul 20, 2023

SQL: joins, window functions python: new column assignment, string and list manipulations How does XGBoost work? step by step process with example How to represent categorical column with high cardinality How can embeddings be generated? How encoder-decoder might help in this How to find presence of multicollinearity in data? What is chi square test of independence? How to handle imbalanced classification? Why is PR auc better suited to such cases then ROC auc? How to handle large no. of classes in multiclass classification? What is negative sampling and how does it help in such scenarios?

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