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Machine Learning Engineer Interview Questions

Review this list of 259 Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • Nvidia logoAsked at Nvidia 

    "Clarifying When we say cloud gaming, we refer to a video gaming experience using cloud computing, right? Assumption: Yes. Understanding of cloud computing first. I'll use some analogies: Imagine you are looking to do heavy computing but don't have a powerful CPU and GPU. CPU and GPU are like your big calculators. You can buy a powerful CPU and GPU, but problems: It costs a lot to buy. It costs a lot to run. You don't need it 24-7. You are not a un"

    Darpan D. - "Clarifying When we say cloud gaming, we refer to a video gaming experience using cloud computing, right? Assumption: Yes. Understanding of cloud computing first. I'll use some analogies: Imagine you are looking to do heavy computing but don't have a powerful CPU and GPU. CPU and GPU are like your big calculators. You can buy a powerful CPU and GPU, but problems: It costs a lot to buy. It costs a lot to run. You don't need it 24-7. You are not a un"See full answer

    Machine Learning Engineer
    Concept
    +3 more
  • Snap logoAsked at Snap 
    Machine Learning Engineer
    Concept
  • Apple logoAsked at Apple 
    Machine Learning Engineer
    Concept
  • Discord logoAsked at Discord 
    Machine Learning Engineer
    Behavioral
    +4 more
  • Google logoAsked at Google 

    "i use google frequently, but most of the time i use for syntax and terms which i dont undestand, google is my go to guy."

    Ankit R. - "i use google frequently, but most of the time i use for syntax and terms which i dont undestand, google is my go to guy."See full answer

    Machine Learning Engineer
    Behavioral
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  • Apple logoAsked at Apple 

    "class TrieNode { constructor() { this.children = {}; this.isEndOfWord = false; } } class Trie { constructor() { this.root = new TrieNode(); } insert(word) { let node = this.root; for (const char of word) { if (!node.children[char]) { node.children[char] = new TrieNode(); } node = node.children[char]; } node.isEndOfWord = true; } search(word) { l"

    Tiago R. - "class TrieNode { constructor() { this.children = {}; this.isEndOfWord = false; } } class Trie { constructor() { this.root = new TrieNode(); } insert(word) { let node = this.root; for (const char of word) { if (!node.children[char]) { node.children[char] = new TrieNode(); } node = node.children[char]; } node.isEndOfWord = true; } search(word) { l"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +3 more
  • Samsung logoAsked at Samsung 

    "I've worked on projects not quite like this, but very similar, in the past - I'll borrow from that to answer this: The Broader Context this problem doesn't specify the type of data we're working with, or how it's being ingested to align with my personal background, I'll assume a picture that lends this problem well to being a computer vision (abbreviated "CV") related question: let's say we have a conveyor belt in a waste facility, which sequentially carries a stream of waste w"

    Zain R. - "I've worked on projects not quite like this, but very similar, in the past - I'll borrow from that to answer this: The Broader Context this problem doesn't specify the type of data we're working with, or how it's being ingested to align with my personal background, I'll assume a picture that lends this problem well to being a computer vision (abbreviated "CV") related question: let's say we have a conveyor belt in a waste facility, which sequentially carries a stream of waste w"See full answer

    Machine Learning Engineer
    Machine Learning
    +1 more
  • Microsoft logoAsked at Microsoft 

    "BERT - bidirectional encoder representations from transformer. For example:- it takes an entire sentence as input at once and understands the meaning of the words in that sentence and calculate the relations of words with each other irrespective of their positions from the original word to understand the meaning of the word using neighboring words. BERT model is a pre trained transformer model which can be fine-tuned for our purposes. It is used for tasks such sentimental analysis, question answ"

    Bhavya V. - "BERT - bidirectional encoder representations from transformer. For example:- it takes an entire sentence as input at once and understands the meaning of the words in that sentence and calculate the relations of words with each other irrespective of their positions from the original word to understand the meaning of the word using neighboring words. BERT model is a pre trained transformer model which can be fine-tuned for our purposes. It is used for tasks such sentimental analysis, question answ"See full answer

    Machine Learning Engineer
    Concept
  • Apple logoAsked at Apple 
    Machine Learning Engineer
    Technical
  • Meta logoAsked at Meta 
    +3

    "I've been a student worker at the Renaissance Hotel, I've been doing Inventory in their closet, stripping rooms, cleaning guest floors and cleaning the fitness center, I've also been a student worker at GFS, My tasks were stocking items on shelf, checking expiration dates, cleaning the breakroom, refilling supplies bottles. Additionally, I've been a student worker at Hampton Inn. The tasks I've completed at Hampton Inn were similar to the Renaissance. The tasks there were stripping rooms, Cleani"

    Amparo L. - "I've been a student worker at the Renaissance Hotel, I've been doing Inventory in their closet, stripping rooms, cleaning guest floors and cleaning the fitness center, I've also been a student worker at GFS, My tasks were stocking items on shelf, checking expiration dates, cleaning the breakroom, refilling supplies bottles. Additionally, I've been a student worker at Hampton Inn. The tasks I've completed at Hampton Inn were similar to the Renaissance. The tasks there were stripping rooms, Cleani"See full answer

    Machine Learning Engineer
    Behavioral
  • Amazon logoAsked at Amazon 
    Machine Learning Engineer
    Concept
    +1 more
  • Walmart Labs logoAsked at Walmart Labs 
    Machine Learning Engineer
    Behavioral
    +5 more
  • Machine Learning Engineer
    System Design
  • Scale AI logoAsked at Scale AI 

    "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved: Image Acquisition:Capturing images or videos using cameras or other imaging devices. Preprocessing steps such as resizing, cropping, and converting color spaces. Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur). Image normalization to standardize pixel values. Contrast e"

    Shibin P. - "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved: Image Acquisition:Capturing images or videos using cameras or other imaging devices. Preprocessing steps such as resizing, cropping, and converting color spaces. Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur). Image normalization to standardize pixel values. Contrast e"See full answer

    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
    Artificial Intelligence
    +2 more
  • Machine Learning Engineer
    Concept
  • Apple logoAsked at Apple 
    Machine Learning Engineer
    Concept
  • Amazon logoAsked at Amazon 

    "DevOps Engineer Interview Questions for 3+ yrs experience candidate"

    Vishwanath K. - "DevOps Engineer Interview Questions for 3+ yrs experience candidate"See full answer

    Machine Learning Engineer
    Concept
  • Adobe logoAsked at Adobe 

    "func isMatch(text: String, pattern: String) -> Bool { // Convert strings to arrays for easier indexing let s = Array(text.characters) let p = Array(pattern.characters) guard !s.isEmpty && !p.isEmpty else { return true } // Create DP table: dpi represents if s[0...i-1] matches p[0...j-1] var dp = Array(repeating: Array(repeating: false, count: p.count + 1), count: s.count + 1) // Empty pattern matches empty string dp[0]["

    Reno S. - "func isMatch(text: String, pattern: String) -> Bool { // Convert strings to arrays for easier indexing let s = Array(text.characters) let p = Array(pattern.characters) guard !s.isEmpty && !p.isEmpty else { return true } // Create DP table: dpi represents if s[0...i-1] matches p[0...j-1] var dp = Array(repeating: Array(repeating: false, count: p.count + 1), count: s.count + 1) // Empty pattern matches empty string dp[0]["See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +3 more
  • Amazon logoAsked at Amazon 
    Machine Learning Engineer
    Behavioral
Showing 161-180 of 259