Meta Machine Learning Engineer Interview Questions
61+ questions from real Meta Machine Learning Engineer interviews, reported by candidates.
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META MLS Full loop interview in 1 week.
I have my coding and AI coding interview in 1 week. Can sommeone suggest best schhedule to follow in thiis last one week.. Assume I do not have much leet code experience, but would be spending hours n
Screening: 衣貳酒: Yuandi + follow-up: Do not change the input 樲咡漆: Yuandi + follow-up: Do not use global variable Onsite: The following content requires a score higher than 150. You can already view it.
Facebook | E5 | July 2020 | Reject
Phone Interview: Odd Even jump kind of problem & an ML algorithm. Onsite: Coding1: Matrix rotation kind of problem, string matching. Coding 2 Bit manipulation, Sort the array by parity type...
First off, I want to say thank you to this community. I am writing this to give back, as I read so many posts here during my preparation that helped me navigate the process. I didn't get the offer, bu
Leetcode is cooked in 2026 and we don't hire people who only grind leetcode.
Edit: yo im not saying NOT to do leetcode, I'm saying don't spend 80% effort on lc and 20% on everything else... About 2 years ago, I got in big tech and thought i've made it. I'm 2400 rating on lc, b
Meta Software Enigneer - Initial Prescreen stage timeline
I recently interviewed for a Production Engineer (University Grad) role. After I was moved to the initial prescreen stage, I submitted the questionnaire within a couple of days and received an invitat
Meta Research Scientist ML 2026
Hi. I am currently in the process of online assessments. Any advice for the online assessments? The online assessment for coding is under CodeSignal Assessment.
Need help to crack META AI Coding round
Hi, I have an upcoming META AI coding round in a month for an ML Engineering manager role. Last I coded was 3 years ago and my coding has been very brute force since I dont come from traditional CS ba
This post was last edited by yah007 on 2025-9-30 19:30 Coding: First Question: LeetCode Yi Si Si Bar Second Question: Similar to LeetCode Yi Ling Ling Si VO: Coding Round 1: LeetCode: Qi Er Yi Er, Sim
Coding: Thirdly, Uncle, one two one, one two three ML Design: Predict whether current users will attend Facebook events Please give me some points, and I'd like to see interview experiences~
I recently confirmed that the latest meta-onsite process has removed the LeetCode round and added an AI coding round. The recruiter said it's too new and there's no feedback, but after confirming seve
**Role:** E6 ML-SWE **Coding Round 1** * **Problem:** Find the K points closest to the origin. * **Solution:** Compute Euclidean distances and use a Max Heap of size K or the QuickSelect algorithm to
Meta E4 Full Loop | Pass & Offer accepted
I completed the Meta E4 Full Loop for Software Engineer, Machine Learning and received an offer. Here you have the process: Phone screen - Coding challenge. Two medium questions from Leetcode Top...
Meta MLE E4/E5 Phone screening | Passed to Full loop
Helping the community Q1. Implement cd. Given current working directory and argument move to final path E.g. cwd - /a/b/c arg - d Output - /a/b/c/d Missed one case while explaining the...
Meta E6 ML Screen | Probable reject
15 min behavioural: Working with cross functional team. Conflict resolution. Coding 339. No variant https://leetcode.com/problems/nested-list-weight-sum/ 528. Variant https://leetcode.com/problems/random-pick-with-weight Gave correct solutions to code but messed up time complexity on the first and forgot...
coding : 1. "Nested List Weight Sum" (LeetCode 339) 2. "986. Interval List Intersections" but instead of finding intersections, we need to merge two sorted, non-overlapping interval lists into a single unioned...
META | E5 MLE | screen
I recently went through the meta screening round. I was not expecting that they will ask hard question in screen round, but there was 1 easy and 1 hard. 1. https://leetcode.com/problems/range-sum-of-bst/ 2. https://leetcode.com/problems/making-a-large-island/description/ feedback...
Meta | Research ML | USA
Phone Screen: Palindrome with k removes Next largest permutation Onsite: Make big island Cheese game Random pick (only return from largest) LCA ML Design: Design ads evaluation framework Behavioral: Asked about my research, why phd? why leave to industry?...
Meta MLE E4/E5 Pass/Fail
Hi, I recently completed my onsite at meta for MLE (E4/E5). Phone Screen Coding: 2 medium -- solved both problems with optimal solutions. Moved to onsite on the next day. Round 1 [coding] 2 medium...
938 Range Sum of BST: https://leetcode.com/problems/range-sum-of-bst/description/ #282 Expression Add Operators: https://leetcode.com/problems/expression-add-operators/description/ but just + and - the hard hurt my soul.. lesson is to study more than a few hards even if...
What Meta Looks for in Machine Learning Engineer Interviews
Meta Machine Learning Engineer interviews are calibrated against the level and scope expected of the role. Across 61+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Meta Machine Learning Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Meta's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Meta Machine Learning Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Meta Machine Learning Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Meta Machine Learning Engineer interview retrospectives on LeakCode.
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