Questions related to basic ML
Learning Engineer Interview Questions
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Core concepts about machine learning. Deep dive into previous ML-related projects and experiences
tell me of a satisfying achievement/impact you had in a recent project
1) Can you explain the difference between the Random Forest and XGBoost algorithms? 2) What are L1 and L2 regularization techniques, and which one would you use for feature selection? 3) What are the different model deployment options available in Amazon SageMaker? 4) How would you monitor a deployed model on SageMaker to ensure its performance over time? 5) Can you tell me about a research paper that you found particularly inspiring or impactful? What made it stand out to you?
Explain about your work at prev org
What do you already know about Recursion?
Most interviews were technical. First interview was high-level algorithmic question about designing an algo to find closest point in 2D map with limited informations, and to reason on the complexity of the algo I come up with.
Personality related stuff (standard wonderlic assesment)
Obviously mainly about ML and DL, some statistics and coding. Questions range from DL details to walking through a case study together.
What is LoRA? Explain back-propagation? What is the variance bias tradeoff?
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