1. Explain supervised and unsupervised ML 2. Scenario-based questions and which ML algorithm to use 3. Justify your answers
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.
8,207 machine learning engineer interview questions shared by candidates
Comment faire du fine tuning d'un LLM ?
Describe RAG , how it works
standard format with Leetcode type questions with test cases, a couple of design rounds. However expectation setting was very poor.
Reverse the string Several ML questions - what is bias and variance, what is the difference between classification and regression, What is the effect on the output feature maps of the convolution layer that follows a max pooling layer. Why do we want a maxpooling layer. When to use a confusion matrix. What is the difference between precision, recall and F1. what is focal loss, what is dice loss. ML System Design (Facial recognition system) - think about clustering, dimensionality reduction, database storage, approximate nearest neighbor search, reducing database lookup time with horizontal scaling. Also think about feature/concept drift detection and rectification. Talk briefly about knowledge distillation, model quantization and pruning. Data engineering question - how would you remove duplicate images? Hash the images using a fingerprinting algorithm using DFT. Similar to how shazam's audio fingerprinting works. Now you have reduced the problem to removing duplicate fingerprints. If you type "image hashing" in google, a lot of academic papers with noise robust algorithms will pop up.
Please introduce your project in your resume.
Q:How would you design a machine learning system for automatically straighten the rotated photos in a dataset?
About your CV/ resume, work
Two eggs and a 100 story building puzzle.
1. Find if a string has balanced paranthesis 2. Basic ML questions (ML algos, evaluation etc)
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