Algorithms
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.
8,202 machine learning engineer interview questions shared by candidates
Technical coding question - Both medium
https://leetcode.com/problems/merge-intervals/ https://leetcode.com/problems/validate-binary-search-tree/ I summarized my experience in the referred post above. Scheduled full loop 2months ahead to have ample time for prep (but couldn't really cover much). First codding Interview: Two questions a. Find the maximum sub-tree sum (I couldn't find related question on leetcode). Essentially consider every node in the tree as the root, them calculate the sum of the nodes, then return the maximum such sum b. Variation of https://leetcode.com/problems/word-break/. Return the output as a concatenation with the words from the dictionary separated by single space. E.g string = "catsanddogs", wordDict = ["cat", "sand","cats","dog","and"], an output = "cat sand dogs" or "cats and dogs" Second coding Interview: Two questions a. Return top k integers with highest frequencies in an array: https://leetcode.com/problems/top-k-frequent-elements/ b. Course schedule: https://leetcode.com/problems/course-schedule-ii/ Behavioural interview + one coding question a. Typical workplace behaviour questions b. Find the length of the longest sub-array whose sum is target System design: Design a Machine Learning app that makes recommendation to users for places to visit along their trip. Focus is no the ML pipeline: Data, Features, Evaluation, Model-building, Feedback, Online testing, Offline testing Personal Assessment: Didn't give good account of myself (first time system design). ML system design: Design E2E classification pipeline for Facebook marketplace. Users post visual (photos) + textual descriptions. Lots of focus on the Model training practice + Feature Engineering + Feedback loop, as well as emphasis on theory Personal Assessment: Way better than 4. Calibration (Behaviour + Coding): a. Behaviour similar to 3 b. Given a linked list L, a value val, and position pos, insert val at the posth position in L (Not replace).
Calculate depth sum of a nested array
Tell me what your resume doesn't say.
Two classic "leetcode" style questions, one involving a binary search and the other dynamic programming. Prepare using the classic resources available online and you will be fine.
Most questions are Leetcode medium level problems
An initial round (background/ profile based), a simple classification problem as homework, a technical interview (mostly based on how the solution was framed for the homework), an interview with a non-technical person, and a final round with their co-founder. 1. Why does multicollinearity happen in regression? 2. Working of boosted trees 3. Types of regularization 4. Overfitting and underfitting
- what is a support vector machine? - what's the difference between a Support Vector Machine (SVM) and a linear classifier? - why do I encode visual data and do not feed the SVM with raw information? - what are the convolutional neural networks and how do they work? - how would I treat a picture of a person that wear zalando clothes? - how could I deal with a multiclass classification problem with 1,000,000 different classes? - how could I possibly help zalando to improve their services?
Burning Wire Problem was asked 3 bulb puzzle was asked
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