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Perplexity AI Machine Learning Engineer Interview Questions

Review this list of 5 Perplexity AI Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • Perplexity AI logoAsked at Perplexity AI 
    Video answer for 'Tell me about yourself.'
    +117

    "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"

    Bipin R. - "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"See full answer

    Machine Learning Engineer
    Behavioral
    +13 more
  • Perplexity AI logoAsked at Perplexity AI 
    +3

    "Since question asks about pipeline. I assume the question is about metrics across many dimensions not just prediction Model performance. For the ML Model: I can use accuracy, precision, recall, F1 if it is classification model. In case it is regression model RMSE is good metric for many problems. Data: ML system needs good quality data. The system has to track missing data rate. Distribution of features, if there is no drift from original feature distributions during the training. Pipeline h"

    Alex N. - "Since question asks about pipeline. I assume the question is about metrics across many dimensions not just prediction Model performance. For the ML Model: I can use accuracy, precision, recall, F1 if it is classification model. In case it is regression model RMSE is good metric for many problems. Data: ML system needs good quality data. The system has to track missing data rate. Distribution of features, if there is no drift from original feature distributions during the training. Pipeline h"See full answer

    Machine Learning Engineer
    Machine Learning
    +1 more
  • Perplexity AI logoAsked at Perplexity AI 
    Machine Learning Engineer
    Artificial Intelligence
    +3 more
  • Machine Learning Engineer
    Artificial Intelligence
    +2 more
  • Machine Learning Engineer
    Artificial Intelligence
    +2 more
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