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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.

8,197 machine learning engineer interview questions shared by candidates

Questions related around my current work and in depth dive into the tools I've been using to orchestrate machine learning pipelines. Since Slalom is a consulting company, they are cloud agnostic. I was more familiar with GCP. What is Vertex AI? What limitations do you see in Vertex AI? How would you create a pipeline in Vertex AI? I think Vertex AI is GCP's service similar to AWS Sagemaker but i might be wrong.
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Machine Learning Engineer

Interviewed at Slalom

3.5★
Aug 20, 2021

Questions related around my current work and in depth dive into the tools I've been using to orchestrate machine learning pipelines. Since Slalom is a consulting company, they are cloud agnostic. I was more familiar with GCP. What is Vertex AI? What limitations do you see in Vertex AI? How would you create a pipeline in Vertex AI? I think Vertex AI is GCP's service similar to AWS Sagemaker but i might be wrong.

Each day a quarry-worker is given a pile of stones and told to reduce the larger stones into smaller ones. The worker must smash the stones together to reduce them, and is told to always pick up the largest two stones and smash them together. If the stones are of equal weight, they both disintegrate entirely. If one is larger, the smaller one is disintegrated and the larger one is reduced by the weight of the smaller one. Eventually, there is either one stone left that cannot be broken, or all of the stones have been smashed. Determine the weight of the last stone, or return O if there is none. Example weights = [1,2,3,6,7,7]. The worker always starts with the two largest stones. In this case, the two largest stones have equal weights of 7 so they both disintegrate when smashed. Next the worker smashes weights 3 and 6. The smaller one is destroyed and the larger weighs 6 - 3 = 3 units. Then, weights 3 and 2 are smashed together, which leaves a stone of weight 1. This is smashed with the last remaining stone of weight 1. There are no stones left, so the remaining stone weight is 0. Function Description Complete the function lastStoneWeight in the editor below. The function must return an integer that denotes the weight of the last stone, or 0 if all stones shattered into dust. lastStoneWeight has the following parameter(s): int weights[n]: an array of integers indicating the weights of each stone Constraints • 1 5n≤ 105 • 1 ≤ weights[i] ≤ 109
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Senior Machine Learning Scientist

Interviewed at Wayfair

3.1★
Sep 3, 2024

Each day a quarry-worker is given a pile of stones and told to reduce the larger stones into smaller ones. The worker must smash the stones together to reduce them, and is told to always pick up the largest two stones and smash them together. If the stones are of equal weight, they both disintegrate entirely. If one is larger, the smaller one is disintegrated and the larger one is reduced by the weight of the smaller one. Eventually, there is either one stone left that cannot be broken, or all of the stones have been smashed. Determine the weight of the last stone, or return O if there is none. Example weights = [1,2,3,6,7,7]. The worker always starts with the two largest stones. In this case, the two largest stones have equal weights of 7 so they both disintegrate when smashed. Next the worker smashes weights 3 and 6. The smaller one is destroyed and the larger weighs 6 - 3 = 3 units. Then, weights 3 and 2 are smashed together, which leaves a stone of weight 1. This is smashed with the last remaining stone of weight 1. There are no stones left, so the remaining stone weight is 0. Function Description Complete the function lastStoneWeight in the editor below. The function must return an integer that denotes the weight of the last stone, or 0 if all stones shattered into dust. lastStoneWeight has the following parameter(s): int weights[n]: an array of integers indicating the weights of each stone Constraints • 1 5n≤ 105 • 1 ≤ weights[i] ≤ 109

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