What concepts of machine learning do I know?
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
Tell me about your projects
The take home challenge had two parts. For the first they give you a dataset (tabular data) about children's vaccinations (which ones, when they were taken) from a low resource country. The goal is to build a model that will predict which children will not complete all their required vaccinations before a certain age. The data is very messy and limited so they want to see how you deal with that too. You also need to provide a write up for next steps. For the second part of the challenge, they give you a healthcare scenario and you are supposed to imagine that you are the lead data scientist on the project and outline in 1-2 paragraphs how you would approach this project and what types of problem statements you would formulate in order to help improve the health supply chain.
Technical ones are: make a function with single user and services input, return the services that will be available to the user the conditions are as follows: some services are only available to some regions, ...
Given an ML dataset, do some analysis and build a model
One thing they asked me was to explain a challenging project I had worked on, detailing the problem, my approach, the tools and technologies I used, and the final outcome.
Most of the questions were heavily related to the details of the test case to check how well thought through my approach was.
Q: How I would plan and structure the new team based on Voi's current organizational setup?
What algorithm would you use for a specific problem they have?
What's the difference between bagging and boosting?
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