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Meta interview notes, page 196 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·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a SWE role. One question, no fluff, just 'design Instagram' and then you're on your own for the next 45 minutes.
  • Design Instagram.

“Broad enough to go in a dozen directions and that's kind of the problem.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a classic maze problem, apparently well-known enough that most of the sub-questions were circulating on 1point3acres beforehand. Five parts total, so if you've done your prep on that forum you'll recognize most of it.
  • Solve a maze traversal problem, broken into five progressive sub-questions.

“The first four parts are basically public knowledge at this point if you've been lurking the usual prep forums.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview focused entirely on a product case around launching group video calls in WhatsApp. Four connected sub-questions that built on each other, from demand analysis to experiment design. Pretty intense for a single case.
  • WhatsApp doesn't have group video calling yet. Using existing call-level data, how would you figure out whether there's real demand for it?
  • What additional quantitative and qualitative data would you want to bring in, and how would you actually use it to inform the decision?
  • After launching group video calls, what success metrics and guardrail metrics would you track? And if you had to pick just one success metric, which would it be and why?
  • Engineering wants to run an experiment before launch. Walk through exactly how you'd design and analyze the A/B test, covering your randomization unit, how you'd handle spillover between users, novelty effects, and what thresholds you'd use for statistical and business significance.

“I started with proxy signals from the existing data, things like how often 1:1 calls happen in group chat contexts, whether people make back-to-back calls to the same set of contacts within a short window, that kind of thing.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Interviewed at Meta for a product role, got a strategy question about Instagram's reactions feature that I thought I was prepared for but kind of fumbled in the middle.
  • How would you decide whether to launch additional reactions on Instagram?

“I started okay, talked about user research and engagement signals, but then I got stuck on how to actually prioritize which new reactions to add versus whether to add any at all.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
A product-heavy DS question at Meta about launching an emoji reaction feature. The whole thing was basically a structured case study spanning metrics, experiment design, risks, and exec communication. Felt more like a PM interview with SQL sprinkled in.
  • A messaging app is launching an emoji reaction feature where users long-press a message to attach an emoji instead of replying with text. As the supporting data scientist, how would you evaluate whether this feature is successful, measure its impact, and present results to C-level executives?
  • What user problem does an emoji reaction feature actually solve, and what hypotheses would you want to test before and after launch?
  • Walk through a metric hierarchy for this feature: primary success metric, leading engagement metrics, downstream product metrics, and guardrail metrics.
  • How would you design the experiment for this launch? Cover randomization unit, interference effects between users in the same conversation, required logging, and analysis window.
  • What are the main risks and negative side effects of this feature? Think about things like reaction cannibalizing text replies, accidental long-presses, notification overload, or uneven effects across user segments.
  • How would you summarize this feature launch to a CEO or other C-suite stakeholders? What would you report, how would you frame business impact, and what recommendation would you make?

“This question is basically five questions dressed up as one.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Meta for a software engineer role, centered entirely on a single deep-dive project question with multiple follow-up layers. The structure was more rigorous than I expected, less "tell me about a time" and more "now justify every decision you made."
  • Walk me through the project you're most proud of. Cover the business and technical context, your specific contribution, the key decisions and trade-offs you made, the measurable outcome, and what you'd do differently.

“This sounds like one question but it's really five questions stitched together, and they will probe each layer separately once you finish your opening.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one algorithmic problem the whole time. The maze rolling-ball thing looks like a BFS/DFS problem but the stopping condition trips you up if you're not careful.
  • Given a 2D grid where 0 is open and 1 is a wall, a ball starts at a given cell and rolls in a chosen direction until it hits a wall or boundary. You can only pick a new direction once the ball stops. Determine whether the ball can stop exactly on a target cell.

“The part that got me was treating the stopping point correctly.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed at Meta for what seemed like a coding/algorithms round. The problem was around maximum profit, probably a stock prices variant. Not much else to go on from what I remember.
  • Given a list of stock prices over time, find the maximum profit you can make from a single buy and sell transaction.

“Classic problem but I still fumbled the edge case where prices only go down.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed at Meta, got asked a behavioral question about experimentation. Pretty standard stuff but it's the kind of question that sounds easy until you're actually sitting there trying to remember a specific story that doesn't sound made up.
  • Tell me about a time you had a great outcome from an experiment you ran.

“I had a story ready but halfway through I realized it wasn't that impressive from a business impact angle.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Screened for a Research Scientist role at Meta and got a pretty basic string manipulation question. Nothing crazy, felt more like a warmup than a real technical bar.
  • Given a string containing letters and other characters, return the difference between the count of uppercase letters and the count of lowercase letters. Non-letter characters should be ignored.

“Straightforward enough.”

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