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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.

8,221 machine learning engineer interview questions shared by candidates

First interviewer tested me on basic probability (Bayes rule etc), binary classification (saw if I knew precision/recall under a heavily unbalanced prior), and signal processing (sampling, aliasing). The coding part was to generate all ordered substrings of a given string. The 2nd interview was a basic test on probability followed by a coding problem (implement linked list, append operation, do in place reversal of list using a single walk through the list). Problems on the final interview were coding problems and basic number theory (implement binary search, implement FIR convolution, estimate frequency of a sine wave signal based on zero crossings, implement divisibility tests for various primes).
avatar

Senior Machine Learning Engineer

Interviewed at SoundHound

3.4★
Jan 16, 2022

First interviewer tested me on basic probability (Bayes rule etc), binary classification (saw if I knew precision/recall under a heavily unbalanced prior), and signal processing (sampling, aliasing). The coding part was to generate all ordered substrings of a given string. The 2nd interview was a basic test on probability followed by a coding problem (implement linked list, append operation, do in place reversal of list using a single walk through the list). Problems on the final interview were coding problems and basic number theory (implement binary search, implement FIR convolution, estimate frequency of a sine wave signal based on zero crossings, implement divisibility tests for various primes).

1. x and y are lists. If x = y and y has been changed, what happens to x? 2. Explain ROS topic latch 3. Explain ROS queue size and how the various data types affect it 4. What are the containers in std standard library and name a few 5. Explain virtual functions in C++ 6. Struct vs Class in C++ 7. Reference vs pointers 8. What's the difference between NumPy max and argmax? 9. Explain multithreading in python and what are its disadvantages compared to C++ 10. Difference between SGD and Adam optimizer. Where do you use them? How does the learning rate work with each? 11. Overfitting vs underfitting in ML 12. Explain batch normalization 13. Difference between shared pointer and unique pointer 14. Techniques to mitigate the overfitting and underfitting 15. Dropout 16. Resnet 17. Difference between different loss function and where do you use them 18. Explain normalization 19. Explain what is public, private , protected in C++
avatar

Software Engineer (Machine Learning)

Interviewed at Continental

3.9★
Dec 7, 2021

1. x and y are lists. If x = y and y has been changed, what happens to x? 2. Explain ROS topic latch 3. Explain ROS queue size and how the various data types affect it 4. What are the containers in std standard library and name a few 5. Explain virtual functions in C++ 6. Struct vs Class in C++ 7. Reference vs pointers 8. What's the difference between NumPy max and argmax? 9. Explain multithreading in python and what are its disadvantages compared to C++ 10. Difference between SGD and Adam optimizer. Where do you use them? How does the learning rate work with each? 11. Overfitting vs underfitting in ML 12. Explain batch normalization 13. Difference between shared pointer and unique pointer 14. Techniques to mitigate the overfitting and underfitting 15. Dropout 16. Resnet 17. Difference between different loss function and where do you use them 18. Explain normalization 19. Explain what is public, private , protected in C++

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