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Meta interview notes, page 218 of 238

FAANG / Big Tech

Most of these Meta write-ups come from Software Engineers (1,194), then Data Scientists (489) and Product Managers (453). The most reported rounds are the technical phone screen (1,190), the product sense round (466), the system design round (255) and the behavioral / leadership round (205).

2377 interview notes · updated Jul 2026

Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta data science interview with a product sense question about feed ranking. One question, open-ended, and it went longer than I expected.
  • You're a data scientist at Instagram. How would you decide the right balance between ads and organic follower posts in the feed, and what metrics would you track to know if your approach is working?

“This question sprawled in a way I wasn't ready for.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta software engineer interview, one coding round focused on a parentheses problem. I fumbled the approach at first and had to pivot mid-interview, which wasn't a great feeling.
  • Given a string with parentheses, remove the minimum number of invalid parentheses to make it valid. Return all possible results.

“Started with a stack because it felt intuitive for parentheses problems, but I quickly realized it wasn't getting me to all valid combinations.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Meta system design round for a software engineer role. The question was a real-time bidding system and the interviewer pushed back hard on my locking strategy, which made things interesting.
  • Design a real-time auction system where users can place bids and always see the current highest bid.

“I went with optimistic locking early on and felt pretty good about it.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML Engineer interview with a coding question that was more about careful implementation than raw algorithmic complexity. The problem itself wasn't hard but the details tripped me up a bit.
  • Given a working maze traversal solution that marks every path cell with '*', modify it so the start and end markers are preserved in the rendered output instead of being overwritten.

“I went straight to storing the endpoint coordinates and checking against them during the marking step, which worked fine.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Did a technical phone screen for an MLE role at Meta. One coding question with a follow-up on complexity, and I fumbled the analysis pretty badly on the follow-up.
  • Find the Kth largest element in an unsorted array. What's the most efficient approach, and can you do better than O(n log k)?

“Got through the main part fine, just a couple of syntax hiccups nothing serious.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Meta system design round, write-heavy focus. Not a lot of detail to go on but it was round 4 so the stakes felt real.
  • Design a write-heavy distributed system.

“This is the kind of question where you can go in a dozen directions and that's kind of the problem.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta MLE interview with a debugging question around card drawing logic. Pretty focused on correctness and edge cases rather than anything flashy.
  • You're given a method that draws three cards from a list representing a table. The method has bugs. Find and fix them so that each card drawn is guaranteed to exist on the table at the moment it's drawn.

“Spent the first few minutes just reading the code slowly because I was sure I was missing something.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE screen with a pretty straightforward coding problem. Nothing fancy, just find the minimum in a list of integers.
  • Given a list of integers, find the minimum element.

“Pretty basic.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
SQL-heavy technical screen for a DS role on the ads measurement side at Meta. Two questions, both tied to the same schema, escalating from a basic share calculation to a full stakeholder-facing time series. Felt more like a take-home prompt than a live interview.
  • Given tables for ad revenue and ad conversions broken out by destination (Facebook Shop vs. external website), write SQL to compute the daily share of revenue going to the Shop destination over the most recent 30 days.
  • Using the same two tables, build a stakeholder-ready daily time series for the past 30 days that evaluates performance by destination. It should include revenue, conversions, revenue per conversion, and at least one share-style diagnostic showing whether volume is shifting toward Shop versus the website.

“Pretty standard pivot-and-divide problem.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta data science interview with a meaty ML evaluation question. The whole thing was pretty focused on one scenario, which sounds simple but the depth they expect is real.
  • A new ML model has been built to flag harmful content. How would you evaluate whether it actually performs better than the current system? Walk through both offline metrics and an online experiment design, and explain how you'd think about the precision-recall trade-off.

“This is the kind of question where you can go in a lot of directions and that's kind of the problem.”

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