Meta Machine Learning Engineer Interview Experiences

Machine Learning Engineer

I interviewed for Software Engineer, Machine Learning at the L5 level. My process was a recruiter screen, an online coding assessment, one live coding round, a second recruiter debrief call, and then a final virtual onsite with three coding interviews, one behavioral, and one system design round. The coding rounds were mostly classic algorithm questions, while the system design was a recommendation system for ranking locations in a feed for someone visiting a new city. I actually felt better about the process than I expected because the interviewers seemed more lenient in how they graded me than I would have graded myself.

• Given two strings, A and B, how do you count the appearances of characters from B in A?
• Find the first common ancestor of two nodes in a binary tree.
• Tell me about a time when you had to change a decision you had previously made.

Machine Learning Engineer

traditional interviews for MLE. coding rounds are AI-enabled meaning you can use claude or codex to write the code.

• Design Instagram.
• Design a payment system.

Machine Learning Engineer

The overall process was rigorous and comprehensive, more challenging than many interviews I have done with other large technology companies. However, the recruiter’s guidance was accurate and genuinely helpful, which I really appreciated. The interviewers were professional and respectful, and I felt the process was designed to evaluate both technical depth and practical problem-solving ability. What went well was that the process gave me opportunities to discuss my experience, research background, and approach to problem solving. What did not go as well was that a few questions caught me off guard, especially where I needed to connect fundamentals, implementation details, and communication under time pressure. Even though I was not able to answer every question as well as I wanted, the experience helped me understand which areas I should strengthen further.

• Given an array of numbers where every number occurs twice except one, how would you find the unique number in O(log n) time?

Machine Learning Engineer

The overall process was rigorous and comprehensive, more challenging than many interviews I have done with other large technology companies. However, the recruiter’s guidance was accurate and genuinely helpful, which I really appreciated. The interviewers were professional and respectful, and I felt the process was designed to evaluate both technical depth and practical problem-solving ability. What went well was that the process gave me opportunities to discuss my experience, research background, and approach to problem solving. What did not go as well was that a few questions caught me off guard, especially where I needed to connect fundamentals, implementation details, and communication under time pressure. Even though I was not able to answer every question as well as I wanted, the experience helped me understand which areas I should strengthen further.

• You are given two arrays of the same length: prices, where prices[i] is the price of item i ratings, where ratings[i] is the rating of item i The value for money of an item is defined as: ratings[i] / prices[i] Return the index of the item with the highest value for money. If multiple items have the same value, return the smallest index.

Machine Learning Engineer

One of the technical rounds was ridiculous, a LC hard (LC3548) ML Systems Design was pretty standard - design a recommender system Behavioral round fairly standard

• Find the maximum subarray sum.
• Merge Intervals
• Design a recommendation engine.

Machine Learning Engineer

It had 1 Phone screen with 2 leetcode style questions the loop had: 1 behavioral 2 leetcode style rounds 1 ml system design (with 1 more follow up)

• Find the sum of all elements in a binary tree that fall within the range [low, high], where 'low' and 'high' are integers.
• Given a grid of size n x n, return all the paths from top-left corner to bottom-right corner. You can only move right or down.
• Count the occurrences of the element in a sorted array.

Machine Learning Engineer

The process was very straightforward. The recruiters are very good. Most of the questions you can find online for coding and they are relatively easy. For the design round, if you practice the ranking questions well, it should cover most of the questions

• Design a ranking system that recommends book titles.

Machine Learning Engineer

The process was very nice, interviewed by people that would not be in my next team, but everybody was exceptionally nice. The questions in the ML system design round were not very deep and were more systems-oriented.

• Given the root of a binary tree, return the length of the diameter of the tree.
• Why do you want to work at Meta?
• Given a non-negative integer x, return the square root of x rounded down to the nearest integer. The returned integer should be non-negative as well.

Machine Learning Engineer

Overall process was easy. Pretty standard questions. Actually was ML focused for an ML candidate. I was able to answer required questions and get he offer.

• Design Facebook Newsfeed.

Machine Learning Engineer

• Build a Calculator
• Given the root of a binary tree, return the vertical order traversal of its node values from top to bottom, column by column. Nodes in the same row and column should be ordered left to right.

Machine Learning Engineer

• Tell me about a time you convinced someone to change their mind.
• Tell me about a time when you had to solve a difficult problem.
• Tell me about a technical challenge that you have overcome.
• What is your favorite Facebook product and why?
• What is your leadership style?
• Trapping Rain Water
• Determine if a given binary tree is a binary search tree (BST).
• Merge k sorted linked lists.
• Why do you want to work at Meta?
• Tell me about your current role.
• Describe your machine learning experience.
• Determine the minimum number of parentheses needed to balance a given string.
• Determine if two words are sorted in lexicographic order.
• Find the dot product of two sparse vectors.
• Design an auto-complete feature.
• Given a Directed Acyclic Graph (DAG), write a function to return the length of the longest path.
• How do you test your machine learning models for production?
• Design the 'Top Picks' feature for Netflix.
• Find the largest perimeter of an island given a two-dimensional array of 1's and 0's representing land and water.
• Design Instagram's Explore page.
• Design an automated comment moderation system.
• Implement Trie
• Efficiently find the next greatest element in an array.
• Find the common ancestors in a tree.
• Design a data structure that supports insert, delete, search, and retrieve in constant time.
• Compute the binary representation of an integer.
• Explain how you handle data scarcity.
• Valid Parentheses
• Design YouTube advertising.
• Design a recommendation engine.
• How do you avoid overfitting a model?