Machine Learning Engineer Interview Questions

Review this list of 237 machine learning engineer interview questions and answers verified by hiring managers and candidates.
  • Adobe logoAsked at Adobe 
    Video answer for 'Given stock prices for the next n days, how can you maximize your profit by buying or selling one share per day?'
    +11

    "public static int maxProfitGreedy(int[] stockPrices) { int maxProfit = 0; for(int i = 1; i todayPrice) { maxProfit += tomorrowPrice - todayPrice; } } return maxProfit; } "

    Laksitha R. - "public static int maxProfitGreedy(int[] stockPrices) { int maxProfit = 0; for(int i = 1; i todayPrice) { maxProfit += tomorrowPrice - todayPrice; } } return maxProfit; } "See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Anthropic logoAsked at Anthropic 
    Machine Learning Engineer
    Concept
    +1 more
  • Goldman Sachs logoAsked at Goldman Sachs 
    +9

    "public static Integer[] findLargest(int[] input, int m) { if(input==null || input.length==0) return null; PriorityQueue minHeap=new PriorityQueue(); for(int i:input) { if(minHeap.size()(int)top){ minHeap.poll(); minHeap.add(i); } } } Integer[] res=minHeap.toArray(new Integer[0]); Arrays.sort(res); return res; }"

    Divya R. - "public static Integer[] findLargest(int[] input, int m) { if(input==null || input.length==0) return null; PriorityQueue minHeap=new PriorityQueue(); for(int i:input) { if(minHeap.size()(int)top){ minHeap.poll(); minHeap.add(i); } } } Integer[] res=minHeap.toArray(new Integer[0]); Arrays.sort(res); return res; }"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +2 more
  • Machine Learning Engineer
    Coding
    +1 more
  • Meta (Facebook) logoAsked at Meta (Facebook) 
    Machine Learning Engineer
    System Design
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  • Amazon logoAsked at Amazon 
    Video answer for 'Implement a k-nearest neighbors algorithm.'
    +4

    "Even more faster and vectorized version, using np.linalg.norm - to avoid loop and np.argpartition to select lowest k. We dont need to sort whole array - we need to be sure that first k elements are lower than the rest. import numpy as np def knn(Xtrain, ytrain, X_new, k): distances = np.linalg.norm(Xtrain - Xnew, axis=1) k_indices = np.argpartition(distances, k)[:k] # O(N) selection instead of O(N log N) sort return int(np.sum(ytrain[kindices]) > k / 2.0) `"

    Dinar M. - "Even more faster and vectorized version, using np.linalg.norm - to avoid loop and np.argpartition to select lowest k. We dont need to sort whole array - we need to be sure that first k elements are lower than the rest. import numpy as np def knn(Xtrain, ytrain, X_new, k): distances = np.linalg.norm(Xtrain - Xnew, axis=1) k_indices = np.argpartition(distances, k)[:k] # O(N) selection instead of O(N log N) sort return int(np.sum(ytrain[kindices]) > k / 2.0) `"See full answer

    Machine Learning Engineer
    Coding
    +2 more
  • Databricks logoAsked at Databricks 
    Video answer for 'What is your leadership style?'
    +7

    "My leadership style is flexible and adaptive, it varies depending on the team members and the needs of the company. My leadership goal is to empower the team and inspire and grow leaders. In order to achieve that, I combine transformational, democratic and coaching leadership styles. Usually when we are facing a new type of challenge, or at the early stage of a project, I like to adapt the transformational leadership which allows me to listen to all the suggestions from the team members and sta"

    onering2ruleall - "My leadership style is flexible and adaptive, it varies depending on the team members and the needs of the company. My leadership goal is to empower the team and inspire and grow leaders. In order to achieve that, I combine transformational, democratic and coaching leadership styles. Usually when we are facing a new type of challenge, or at the early stage of a project, I like to adapt the transformational leadership which allows me to listen to all the suggestions from the team members and sta"See full answer

    Machine Learning Engineer
    Behavioral
    +4 more
  • Apple logoAsked at Apple 

    "We have a list of documents. We want to build an index that maps keywords to documents containing them. Then, given a query keyword, we can efficiently retrieve all matching documents. docs = [ "Python is great for data science", "C++ is a powerful language", "Python supports OOP and functional programming", "Weather today is sunny", "Weather forecast shows rain" ]"

    Mridul J. - "We have a list of documents. We want to build an index that maps keywords to documents containing them. Then, given a query keyword, we can efficiently retrieve all matching documents. docs = [ "Python is great for data science", "C++ is a powerful language", "Python supports OOP and functional programming", "Weather today is sunny", "Weather forecast shows rain" ]"See full answer

    Machine Learning Engineer
    Coding
    +1 more
  • "Write a function which Caesar ciphers all the strings so that the first character is "a". Use ascii code points and the modulo operator to do this. Use this function to create a hashmap between each string and the CC-a string. Then go through each key:value pair in the hashmap, and use the CC-a ciphered value as the key in a new defaultdict(list), adding the original string to the value field in the output."

    Michael B. - "Write a function which Caesar ciphers all the strings so that the first character is "a". Use ascii code points and the modulo operator to do this. Use this function to create a hashmap between each string and the CC-a string. Then go through each key:value pair in the hashmap, and use the CC-a ciphered value as the key in a new defaultdict(list), adding the original string to the value field in the output."See full answer

    Machine Learning Engineer
    Coding
    +1 more
  • Airbnb logoAsked at Airbnb 
    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Machine Learning Engineer
    System Design
  • Adobe logoAsked at Adobe 
    Video answer for 'Given the root of a binary tree of integers, return the maximum path sum.'

    "\# Definition for a binary tree node. class TreeNode: def init(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def maxPathSum(self, root: TreeNode) -> int: self.max_sum = float('-inf')"

    Jerry O. - "\# Definition for a binary tree node. class TreeNode: def init(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def maxPathSum(self, root: TreeNode) -> int: self.max_sum = float('-inf')"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Adobe logoAsked at Adobe 
    Video answer for 'Given an nxn grid of 1s and 0s, return the number of islands in the input.'
    +10

    " from typing import List def getnumberof_islands(binaryMatrix: List[List[int]]) -> int: if not binaryMatrix: return 0 rows = len(binaryMatrix) cols = len(binaryMatrix[0]) islands = 0 for r in range(rows): for c in range(cols): if binaryMatrixr == 1: islands += 1 dfs(binaryMatrix, r, c) return islands def dfs(grid, r, c): if ( r = len(grid) "

    Rick E. - " from typing import List def getnumberof_islands(binaryMatrix: List[List[int]]) -> int: if not binaryMatrix: return 0 rows = len(binaryMatrix) cols = len(binaryMatrix[0]) islands = 0 for r in range(rows): for c in range(cols): if binaryMatrixr == 1: islands += 1 dfs(binaryMatrix, r, c) return islands def dfs(grid, r, c): if ( r = len(grid) "See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Adobe logoAsked at Adobe 
    Video answer for 'Find a triplet in an array with a given sum.'
    +9

    " import java.util.*; class Solution { // Time Complexity: O(n^2) // Space Complexity: O(n) public static List> threeSum(int[] nums) { // Ensure that the array is sorted first Arrays.sort(nums); // Create the results list to return List> results = new ArrayList(); // Iterate over the length of nums for (int i = 0; i < nums.length-2; i++) { // We will have the first number in"

    Victor O. - " import java.util.*; class Solution { // Time Complexity: O(n^2) // Space Complexity: O(n) public static List> threeSum(int[] nums) { // Ensure that the array is sorted first Arrays.sort(nums); // Create the results list to return List> results = new ArrayList(); // Iterate over the length of nums for (int i = 0; i < nums.length-2; i++) { // We will have the first number in"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +3 more
  • Capital One logoAsked at Capital One 
    Machine Learning Engineer
    System Design
  • OpenAI logoAsked at OpenAI 
    Machine Learning Engineer
    Behavioral
    +5 more
  • "Problem: Given an input string txt consisting of alphanumeric characters and the parentheses characters '(' & ')', write a function which removes the minimum number of characters to return a version of the string with properly balanced parenthesis. Answer: You can do this with a counter. Psuedo-Python Start with counter = 0 output = [] Iterate through the string, every time you encounter a '(', increment the counter. Add the character to the output. If you encounter a ')', decrement the coun"

    Michael B. - "Problem: Given an input string txt consisting of alphanumeric characters and the parentheses characters '(' & ')', write a function which removes the minimum number of characters to return a version of the string with properly balanced parenthesis. Answer: You can do this with a counter. Psuedo-Python Start with counter = 0 output = [] Iterate through the string, every time you encounter a '(', increment the counter. Add the character to the output. If you encounter a ')', decrement the coun"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +1 more
  • Apple logoAsked at Apple 
    +10

    " class ListNode: def init(self, val=0, next=None): self.val = val self.next = next def has_cycle(head: ListNode) -> bool: pass # your code goes here if head is None: return False previousNodes = set() iter = head while iter: if iter.val in previousNodes: return True previousNodes.add(iter.val) iter = iter.next; return False debug your code below node1 = ListNode(1) node2 = ListNode(2) n"

    Cagdas A. - " class ListNode: def init(self, val=0, next=None): self.val = val self.next = next def has_cycle(head: ListNode) -> bool: pass # your code goes here if head is None: return False previousNodes = set() iter = head while iter: if iter.val in previousNodes: return True previousNodes.add(iter.val) iter = iter.next; return False debug your code below node1 = ListNode(1) node2 = ListNode(2) n"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Adobe logoAsked at Adobe 
    +7

    "function findPrimes(n) { if (n < 2) return []; const primes = []; for (let i=2; i <= n; i++) { const half = Math.floor(i/2); let isPrime = true; for (let prime of primes) { if (i % prime === 0) { isPrime = false; break; } } if (isPrime) { primes.push(i); } } return primes; } `"

    Tiago R. - "function findPrimes(n) { if (n < 2) return []; const primes = []; for (let i=2; i <= n; i++) { const half = Math.floor(i/2); let isPrime = true; for (let prime of primes) { if (i % prime === 0) { isPrime = false; break; } } if (isPrime) { primes.push(i); } } return primes; } `"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • +1

    "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"

    Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias. Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit. There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer

    Machine Learning Engineer
    Concept
    +2 more
Showing 81-100 of 237