Can't go into specifics but the initial interviews focused on engineering fundamentals and leetcode type questions while the later interviews focused more on Machine Learning fundamentals and company fit.
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.
8,208 machine learning engineer interview questions shared by candidates
Tell me about yourself and explain ur projects
How can we stop overfitting? A simple problem for calculating precision and recall was also asked.
Define a CNN, how it works and which parts of the entire CNN operation benefit most from GPU usage.
Probability question. Every 15 minutes, 20% chance of seeing one shooting star, what's the chance of seeing at least 1 shooting star in 60 mins.
Grid manipulation and graph traversal
1. Tell me about a time when to solved a problem?
- 3rd round, take home task. I have spent hours and completed the challenge. Hiring team didn't bother to send even a single line of feedback. Such an unprofessionalism.
What would you do if you have more time in work？
Was given a set of annotation guidelines for NER and was asked how I would improve them / what is missing etc.
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