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Meta interview notes, page 195 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·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Meta that focused on Spotify Podcasts as a case study. Two connected questions, one on defining success metrics and a follow-up on how to respond to a trade-off scenario between Podcasts and Music engagement.
  • As a PM for Spotify Podcasts, what success metrics would you define?
  • If engagement on Spotify Music is down while engagement on Spotify Podcasts is up, what actions would you take?

“I went straight for engagement metrics like listen time and episode completion rate, then layered in retention and subscriber growth.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta product sense round, one question about the Facebook login product. Not a lot of context to go on but it's the kind of question that looks simple until you realize how much surface area there actually is.
  • How would you improve the Facebook login product?

“I went straight to friction reduction and passkeys, which felt smart at the time, but I barely touched on the trust and security angle and that's probably where Meta actually cares most.”

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Meta·Data Scientist·Onsite - System Design / Architecture

Jun 2026
Brutal Meta DS onsite question about staging a feature rollout using only pre-launch data. The whole thing was one long end-to-end ML design problem and it went in a lot of directions fast.
  • You need to predict which users or products would benefit most from a new 'More like this' feature before it launches globally. Using only pre-launch interaction and product data plus any logging you can add at launch, design an end-to-end modeling approach covering: proxy labeling and post-launch true labels (with target leakage prevention), feature engineering including cross-features and cold-start handling, two candidate model families with calibration considerations, offline evaluation strategy and shadow testing, and a deployment policy that addresses fairness, negative spillovers, and ongoing drift monitoring.

“This was basically five questions crammed into one.”

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Meta·Software Engineer·Online Assessment (OA)

Jun 2026
Meta SWE online assessment with a maze solver problem split into multiple stages. The first part was debugging broken output code, then building out a full BFS-based solver on top of it. Read the source files carefully or you will waste a lot of time.
  • Given a maze represented as a 2D grid, first fix the existing buggy code that incorrectly draws the maze output, then implement a maze solver using BFS across subsequent test stages.

“The bug fix part tripped me up more than I expected.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a product analytics question about evaluating a new group video feature. Pretty open-ended, which I wasn't fully prepared for. The hints they give you in prep material about 'call quality metrics' undersell how much they want you to think about experimentation design specifically.
  • If we launched a group video calling feature on a social media platform, how would you measure whether it was successful? Walk through the metrics you'd track, how you'd design an A/B test, what success looks like, and any trade-offs worth calling out.

“I started with engagement metrics and felt okay there.”

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Meta·Software Engineer·Recruiter / HR Screen

Jun 2026
Quick Meta SWE screen, basically just one behavioral question about job motivation. Not much to report.
  • What's prompting you to look for a new role at this point in your career?

“Pretty standard opener but I fumbled it a bit.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round for a Research Scientist role at Meta. Just the one question about feedback, pretty standard stuff, nothing too surprising.
  • Tell me about a time you received critical feedback. How did you handle it?

“I had a story ready but rambled a bit trying to show I was self-aware without sounding defensive.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, just one question on trees. Pretty standard but I fumbled the edge cases more than I'd like to admit.
  • Given a binary tree and two node values, find their lowest common ancestor.

“Knew this one but still second-guessed myself mid-solution.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE phone screen with a parentheses problem that's basically a twist on a classic LC question. Pretty straightforward if you've seen the original, but the output requirement is different so it's easy to overthink it.
  • Given a string of lowercase letters and parentheses, return the minimum number of parentheses you need to remove to make the string valid (every open paren has a matching close paren, properly nested).

“I recognized it as a variant of the minimum-remove parentheses problem but spent a few seconds second-guessing myself because they only wanted a count, not the reconstructed string.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a system design spin on a classic algorithm problem. The twist was having to think through read-heavy versus write-heavy tradeoffs rather than just solving the coding part.
  • Design a system to find the top K frequent elements from an array of integers. How does your approach change depending on whether the system is read-heavy versus write-heavy?

“I went straight to the heap-based solution because that's what I'd drilled, but then the read vs write framing made me realize I'd only answered half the question.”

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