Machine Learning Engineer Interviews

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.

8,212 machine learning engineer interview questions shared by candidates

## 1. Risk ### Part A The board game Risk has simple combat rules: an invading force of 1 to 3 units attacks a defending force of 1 to 3 units. Each invading unit rolls a 6-sided die, and each defending unit rolls a 6-sided die. The highest numbered invading die matches to the highest numbered defending die, then the next highest, etc., as long as there are two dice to match up. For example, if there are two attacking dice and one defending die, only one from each side match up. The side with the higher number on each match up wins, with the defending die winning the tie. Write a function that returns a fair, random `(invading_wins, defending_wins)` for an input `(invading_count, defending_count)`: ```python def random_outcome(invading_count, defending_count): # ... return (invading_wins, defending_wins) ``` Example cases are below. ```python >>> random_outcome(2, 1) (1, 0) >>> random_outcome(2, 1) (0, 1) >>> random_outcome(2, 2) (1, 1) >>> random_outcome(2, 2) (0, 2) >>> random_outcome(2, 2) (2, 0) >>> random_outcome(3, 1) (1, 0) >>> random_outcome(3, 1) (0, 1) ``` ### Part B. Using this function, write a script that prints a good estimate for the probabiltiy of the invader winning at least one for each of the 9 cases of 1..3 invaders and 1..3 defenders. Averaging the result of 1000 evaluations for the same input is good enough estimate of the outcome. Example output is below. ``` with 1 invader and 1 defender, the probably of the invader winning at least one is about 41% ```
avatar

Machine Learning Engineer

Interviewed at HeadSpin

3.7
May 15, 2020

## 1. Risk ### Part A The board game Risk has simple combat rules: an invading force of 1 to 3 units attacks a defending force of 1 to 3 units. Each invading unit rolls a 6-sided die, and each defending unit rolls a 6-sided die. The highest numbered invading die matches to the highest numbered defending die, then the next highest, etc., as long as there are two dice to match up. For example, if there are two attacking dice and one defending die, only one from each side match up. The side with the higher number on each match up wins, with the defending die winning the tie. Write a function that returns a fair, random `(invading_wins, defending_wins)` for an input `(invading_count, defending_count)`: ```python def random_outcome(invading_count, defending_count): # ... return (invading_wins, defending_wins) ``` Example cases are below. ```python >>> random_outcome(2, 1) (1, 0) >>> random_outcome(2, 1) (0, 1) >>> random_outcome(2, 2) (1, 1) >>> random_outcome(2, 2) (0, 2) >>> random_outcome(2, 2) (2, 0) >>> random_outcome(3, 1) (1, 0) >>> random_outcome(3, 1) (0, 1) ``` ### Part B. Using this function, write a script that prints a good estimate for the probabiltiy of the invader winning at least one for each of the 9 cases of 1..3 invaders and 1..3 defenders. Averaging the result of 1000 evaluations for the same input is good enough estimate of the outcome. Example output is below. ``` with 1 invader and 1 defender, the probably of the invader winning at least one is about 41% ```

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