There were coding questions with a very limited timing
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.
8,197 machine learning engineer interview questions shared by candidates
What are my experiences and how can i add value to the company.
How would you deal with an imbalanced dataset?
Was asked about feature processing techniques, neural networks, broadcasting, etc.
they ask basic question
General ML questions: explain how algorithm x works, how would you generalise this approach to a production problem, what considerations should be taken in terms of training properly.
The problem was to calculate K nearest neighbors for each of their 2D arrays given another array. Using python libraries was allowed.
Typical coding interview stuff. Machine learning and information retrieval theory.
Aptitude test was good and simple especially psychometric 10 questions.Round 2 get tough and python 70% and c 30% quesions are asked.Not MCQ but answer the output.
Personality questions, inquiries about my previous work and research. You also receive a task to work on at home.
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