What's your favorite algorithm?
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.
8,203 machine learning engineer interview questions shared by candidates
Code review on 50 lines of python code
Pagination with Pivoting: You will be given a list of items, and the aim is to implement pagination around a pivot element. It should be circular.
Second Interview Question: Given a 2D binary matrix, write a solution to make the image symmetric along the X and Y axes. The only operations allowed are full row and full column insertions without modifying the values in the original matrix. The goal is to find the minimum number of row and column insertions.
Questions will be based on the take-home technical assessment.
The programming test was written: three exercises.
Resume, Spring, ML, Data Science, Python and Behavioural
A couple interview Qs I can remember are: What is the bias-variance tradeoff? What's a GBM and an example of one? What is under fitting and over fitting?
Sparse metric Multiplication, cosine Similarity
Which would run faster, a RNN with LSTM or Attention (he meant Attention based Model such as Transformers but didn't clarify).
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