Meta Machine Learning Engineer Interview Experiences & Questions
9 candidates share their Meta Machine Learning Engineer interviews: each round, the questions asked and the outcome. Latest: Jul 2026.
What to expect · Meta Machine Learning Engineer
Based on 9 real interviews
The Meta Machine Learning Engineer interview process
Most common round types: Coding (6), Behavioral (5), Machine Learning (5), Data Structures & Algorithms (3), Project Discussion (3), System Design (3).
Real Meta Machine Learning Engineer interview questions
- Implement an efficient sparse matrix times vector multiplication.
- Describe your current role, team, project work, tools, AI usage, preferred language, and coding versus leadership split.
- Given an array of numbers where every number occurs twice except one, how would you find the unique number in O(log n) time?
- Walk me through your experience and what you delivered in your previous projects.
- How would you design a system like Google Street View?
- How would you approach depth from a single image?
- Here is how you were evaluated in the first coding rounds and what to expect in the onsite.
- Implement a k-nearest neighbor search.
- Tell me about a time you were under schedule pressure to deliver something you had committed to.
- General questions about experience
- Can you walk me through your previous experience, including projects you've worked on and what you studied?
- Design TikTok's video recommendation system.
- What's the general idea for the third question?
- Tell me about a time you had a conflict with a member of your team. What was the conflict and how did you handle it?
- Find the maximum subarray sum.
- Can you walk me through your background and why it matches this computer vision role?
- Simplify a path string and return the valid path.
- Tell me about a time you got a less than stellar review from your manager. How did you handle it and how did you grow from it?
- Merge Intervals
- Given two sorted interval lists, merge them.
- Design Instagram.
- Design a recommender system.
- Find the common ancestor given nodes in a tree with parent pointers.
- Tell me about the project you're most proud of.
- Design a ranking system that recommends book titles.
- Given a sorted array and a value k, find the next element greater than k.
- Tell me about a time you disagreed with someone and you were wrong.
- Run the tests, debug the codebase, implement the missing core function, and use the LLM if helpful.
- Tell me about a conflict within a team and how you handled it.
- Design a recommendation system for locations in a feed for a user who arrives in a new city and wants recommendations for places to visit.
- Tell me about a time you disagreed with your manager and how you handled it.
- Design a payment system.
- Why do you want to work at Meta?
- 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…
- Build functions to navigate a file directory structure.
- Given two strings A and B, identify all the characters in A that are also present in B.
- Traverse a matrix or grid to find the shortest path between point A and point B.
- Given a string containing round, square, and curly parentheses, how many of them are mismatched?
- Given a tree and any two nodes, find their first shared parent node.
- Keep track of the k largest elements of a running list.
Showing 40 of 49 questions. Open any experience for every question and how the candidate answered.
All 9 Meta Machine Learning Engineer interview experiences
“What caught me off guard was that one of the coding rounds was not generic LeetCode at all. It was AI coding, with things like sparse matrix times vector multiplication and k-nearest neighbor…”
“traditional interviews for MLE. coding rounds are AI-enabled meaning you can use claude or codex to write the code.”
“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…”
“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…”
“The AI enabled round was actually pretty unique. I used bit mask first, then hit a collision because the input had letters and numbers, and the LLM started hallucinating new functions, so I had to…”
“I got feedback that I performed well on the coding interviews even when I did not feel like I performed up to a requisite level, so I think the bar has more flexibility than we may give ourselves…”
“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”
“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…”
“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…”
More: all Meta interviews · Machine Learning Engineer interviews at every company
Frequently asked questions
How many rounds are in the Meta Machine Learning Engineer interview?
Candidates report a typical 2 rounds, usually including a recruiter screen, online assessment, phone screen, technical round.
How hard is the Meta Machine Learning Engineer interview?
Candidates rate it 3.3/5 on average (medium), across 9 interviews.
How long does the Meta Machine Learning Engineer hiring process take?
About 4 weeks from first contact to decision, based on reported timelines.
What percentage of Meta Machine Learning Engineer candidates get an offer?
56% of candidates with a final result got an offer (5 of 9).
What does the Meta Machine Learning Engineer interview focus on?
The most common round types are Coding, Behavioral, Machine Learning, Data Structures & Algorithms.