Data structures: internals of a hashmap, linkedList vs arrayList, TreeSet vs HashSet, LinkedSet vs TreeSet, PrefixTree (Trie)
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.
8,205 machine learning engineer interview questions shared by candidates
construct a join between 3 tables with some constraints
wavenet, time seriers
I appreciated the depth to which various interviewers wanted to understand what I was looking to get out of a role with PhysicsX, and ensure that we were a good fit for each other.
This was effectively a ML system design question for a entry level position. It's not hard to guess what one could be given that they are effectively trying to solve problems in supply chain. Hint: Try to look up ML system design questions at companies like Uber.
Technical coding test using HackeRank
For hacker rank I solved two basic programming questions. One about accessing an array and another filling missing data with averages.
Asked some basic Python questions about syntax and generators. Take-home assessment was about 5 hours long and consisted of a supervised ML problem. Incorporated skills that they looked for in an ML SWE.
Tell me about a time when you learned a new technology to solve a problem.
How to vectorise a certain set of operations using Numpy.
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