Netflix Interview Questions

Review this list of 25 Netflix interview questions and answers verified by hiring managers and candidates.
  • "Questioning ones own self in the 1st, 2nd person."

    Kaleb A. - "Questioning ones own self in the 1st, 2nd person."See full answer

    Behavioral
  • Netflix logoAsked at Netflix 

    "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them. I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."

    Xuelong A. - "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them. I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."See full answer

    Machine Learning Engineer
    Behavioral
  • Netflix logoAsked at Netflix 

    "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING: TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency. The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"

    Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING: TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency. The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer

    Machine Learning Engineer
    Concept
  • Netflix logoAsked at Netflix 
    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
    Concept
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