Machine Learning Engineer Interview Questions

Team Glider

Updated on March 2, 2023

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Machine Learning Engineer Position Overview

Big Data has evolved to become the hottest new trend in the tech industry, making way for machine learning. Machine learning is incredibly useful for making predictions or suggestions based on massive amounts of data. Engineers specializing in machine learning have skills that are relevant to a Data Scientist but are focused more on design and application of the model.

With expertise in research, coding, and data science, Machine Learning Engineers run the operations of a project, manage the infrastructure and data pipelines, and help bring the code to proper executing. They are at the center of every machine learning project, handling the heavy-lifting.

Proficiency in optimization, statistics, data mining and algorithm design are some other pre-requisites for the role of Machine Learning Engineers.

Qualifications Required

  • Masters or Ph.D. Degree in Computer Science or Mathematics

Experience Required

  • Experience in working with tools and packages for machine learning
  • Experience in computer programming
  • Expertise with UNIX tools
  • Knowledge of tools and packages such as Spark ML, scikit-learn (Python), Mahout and R, etc

Skills Required

  • Data modeling and evaluation
  • Computer science fundamentals and programming
  • Strong mathematical skills
  • Software engineering and system design.

Machine Learning Engineer Role-specific interview questions

Machine Learning Engineer Theoretical interview questions

  • What is the trade-off between variance and bias?
  • How is KNN different from k-means clustering?
  • What is Bayes’ Theorem? How is it related to the context of machine learning?
  • What is the difference between Type 1 and Type 2 error?
  • What do you mean by the Kernel trick?

Machine Learning Engineer Model interview questions

  • What type of problems can a model try to solve?
  • Is the model interpretable?
  • Does model have any meta-parameter?
  • How do you handle missing data in a corrupted dataset?
  • Is a model prone to overfitting? What can you do about this?
  • What type of data fields can a model handle?
  • Where do you usually source datasets from?
  • How do you ensure that you are not overfitting a model?

Machine Learning Engineer interview questions

  • What do you mean by deep learning? How does it contrast with other traditional machine learnings?
  • What is EM algorithm and its applications?
  • What is a Fourier Transform?
  • What does ‘linear’ mean in a generalized linear model?
  • State an example of an application of non-negative matrix factorization.
  • Describe probabilistic graphical model.
  • How Bayesian networks differ from Markov networks?
  • What are the effective tactics for performing feature selection that does not involve exhaustive search?
  • What are the methods for dimensionality reduction?

Research-based interview questions

  • Do you have any experience with Spark ML or any other platform for developing machine learning models?
  • Which tools have you used to train and evaluate models?
  • Do you hold any research experience in machine learning or relevant field?

Industry specific interview questions

  • What are your views on our current data process?
  • How can we use your machine learning skills to generate revenue?
  • How will you implement a recommendation system for our users?

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