Machine Learning Engineer Interview Questions

Review this list of 209 machine learning engineer interview questions and answers verified by hiring managers and candidates.
  • Google logoAsked at Google 
    Machine Learning Engineer
    Behavioral
  • Adobe logoAsked at Adobe 

    "static boolean sudokuSolve(char board) { return sudokuSolve(board, 0, 0); } static boolean sudokuSolve(char board, int r, int c) { if(c>=board[0].length) { r=r+1; c=0; } if(r>=board.length) return true; if(boardr=='.') { for(int num=1; num<=9; num++) { boardr=(char)('0' + num); if(isValidPosition(board, r, c)) { if(sudokuSolve(board, r, c+1)) return true; } boardr='.'; } } else { return sudokuSolve(board, r, c+1); } return false; } static boolean isValidPosition(char b"

    Divya R. - "static boolean sudokuSolve(char board) { return sudokuSolve(board, 0, 0); } static boolean sudokuSolve(char board, int r, int c) { if(c>=board[0].length) { r=r+1; c=0; } if(r>=board.length) return true; if(boardr=='.') { for(int num=1; num<=9; num++) { boardr=(char)('0' + num); if(isValidPosition(board, r, c)) { if(sudokuSolve(board, r, c+1)) return true; } boardr='.'; } } else { return sudokuSolve(board, r, c+1); } return false; } static boolean isValidPosition(char b"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Amazon logoAsked at Amazon 
    Machine Learning Engineer
    Behavioral
  • Meta (Facebook) logoAsked at Meta (Facebook) 
    Machine Learning Engineer
    Data Structures & Algorithms
    +1 more
  • Google logoAsked at Google 

    "Depends on the memory available. Also if there's any structure to the data already. If this is just pure numbers random and all over the place and we don't need to worry about space. Then radix sort would be the best. But this all depends on what constraints the interviewer gives you."

    Ted K. - "Depends on the memory available. Also if there's any structure to the data already. If this is just pure numbers random and all over the place and we don't need to worry about space. Then radix sort would be the best. But this all depends on what constraints the interviewer gives you."See full answer

    Machine Learning Engineer
    Analytical
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  • TikTok logoAsked at TikTok 
    Machine Learning Engineer
    Concept
  • Meta (Facebook) logoAsked at Meta (Facebook) 

    "HashMap supports insert, search, delete and retrieve in O(1). It stores data as key value pairs."

    Ina K. - "HashMap supports insert, search, delete and retrieve in O(1). It stores data as key value pairs."See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +1 more
  • Adobe logoAsked at Adobe 
    +7

    "from typing import List def traprainwater(height: List[int]) -> int: if not height: return 0 l, r = 0, len(height) - 1 leftMax, rightMax = height[l], height[r] res = 0 while l < r: if leftMax < rightMax: l += 1 leftMax = max(leftMax, height[l]) res += leftMax - height[l] else: r -= 1 rightMax = max(rightMax, height[r]) "

    Anonymous Roadrunner - "from typing import List def traprainwater(height: List[int]) -> int: if not height: return 0 l, r = 0, len(height) - 1 leftMax, rightMax = height[l], height[r] res = 0 while l < r: if leftMax < rightMax: l += 1 leftMax = max(leftMax, height[l]) res += leftMax - height[l] else: r -= 1 rightMax = max(rightMax, height[r]) "See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • 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
  • 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]["

    Vince 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
  • Scale AI logoAsked at Scale AI 
    Machine Learning Engineer
    Behavioral
  • Amazon logoAsked at Amazon 
    Machine Learning Engineer
    Concept
  • Pinterest logoAsked at Pinterest 
    Machine Learning Engineer
    Behavioral
  • Pinterest logoAsked at Pinterest 

    "The difference between convex and nonconvex functions lies in their mathematical properties and the implications for optimization problems. Convex Functions:A convex function has a shape where any line segment connecting two points on its graph lies entirely above or on the graph. This property ensures that any local minimum is also a global minimum, making optimization straightforward and reliable. Convex functions are critical in machine learning and optimization tasks because of th"

    Alan T. - "The difference between convex and nonconvex functions lies in their mathematical properties and the implications for optimization problems. Convex Functions:A convex function has a shape where any line segment connecting two points on its graph lies entirely above or on the graph. This property ensures that any local minimum is also a global minimum, making optimization straightforward and reliable. Convex functions are critical in machine learning and optimization tasks because of th"See full answer

    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
    Coding
    +1 more
  • Adobe logoAsked at Adobe 

    "simply check its size if the size if the size is greater than n then yes it has duplicate"

    Kunal kumar S. - "simply check its size if the size if the size is greater than n then yes it has duplicate"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +2 more
  • Amazon logoAsked at Amazon 

    "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"

    Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer

    Machine Learning Engineer
    Concept
  • Capital One logoAsked at Capital One 
    Machine Learning Engineer
    Machine Learning
  • Netflix logoAsked at Netflix 
    Machine Learning Engineer
    Concept
  • JP Morgan Chase logoAsked at JP Morgan Chase 
    Machine Learning Engineer
    Concept
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