ML questions were very basic, e.g. logistic regression, unbalanced classes in binary classification Coding Q was length of longest integer sublist with sum 0.
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
What is a decision tree?
Describe the project in my resume
What is SOLID in programming?
A coding question in graph theory
The interviews mostly focused on past systems experience. They had me describe the architecture of a couple systems I'd previously built, and then dug into the details, tradeoffs, and lessons learned for a long time. Additionally there were a couple of short whiteboarding questions, one that was all about probability and another that focused on algorithm design. It seemed like the most important part of these was actually thinking through them, asking good questions along the way, and collaborating with the interviewer.
- How to improve the performance of your code? - How does JIT work.
STAR Questions, Huge GIS-based take-home project, LeetCode style coding interview.
Take-home assignment involving manipulating raster data files
What is neural search? Have you ever built a neural search algorithm? How does classical search work?
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