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Meta interview notes, page 44 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 - Coding / Algorithms

Apr 2026
Meta SWE coding round, got a maze traversal problem that looked like basic BFS until you realize you have to track key state too. Not a question you want to see if you've only ever done vanilla shortest path stuff.
  • Given a grid maze with a start, an end, walls, keys (a-f), and locked doors (A-F), find the minimum number of steps from start to end moving in 4 directions. A door can only be passed if you're carrying the corresponding key. Return -1 if the end is unreachable.

“I started with regular BFS and got maybe two minutes in before realizing the visited set was wrong.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Meta SWE coding round, one problem the whole session. It started simple enough and then they pushed it into 2D territory which is where things got more interesting.
  • Given an immutable 2D binary matrix, design a data structure that preprocesses it once and then answers queries of the form: how many 1s are in the rectangular subregion defined by (r1, c1) to (r2, c2)?

“The 1D version of this is pretty textbook so I got there fast.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview with a meaty experiment design question about group call participant caps. One question, but it had like five sub-parts and covered basically everything from randomization to stopping rules. Felt more like a take-home prompt delivered verbally.
  • You need to decide the maximum number of participants allowed per Group Call, choosing between caps of 4, 8, or 16. Design an experiment to make that decision. Walk through: your choice of fixed multi-arm vs adaptive design given a 4-week timeline, your primary metric and reliability guardrails, how you'd stratify randomization across device class, network type, and region, how you'd detect non-monotonic effects and segment differences, and your stopping rules including rollback thresholds if latency SLOs are breached.

“This one sprawled.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Brutal A/B testing design question for a DS role at Meta. The whole interview was basically one giant multi-part experiment design problem and it went deep fast.
  • Design an A/B test to measure the causal effect of adding a 'Work from home' filter on a marketplace search page, where the primary metric is bookings per visit. Walk through hypothesis setup, randomization unit, metric definition, variance reduction, sample size, and risk mitigation.

“This question is basically ten questions stapled together.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview focused entirely on retention metrics, which sounds straightforward until you're 20 minutes deep explaining the difference between N-day and rolling retention to someone who clearly knows the answer. Heavy on product analytics and experimentation design, less coding than I expected.
  • What are the main ways to define user retention, and how do N-day retention, rolling retention, and return rate differ from each other?
  • How would you compute 7-day versus 28-day retention, and what does each one actually tell you about user behavior?
  • Can you give examples of product changes that would boost 7-day retention but hurt 28-day retention, or the reverse? And how would you structure a metric hierarchy around retention?
  • What are the main pitfalls when measuring retention, and how do you handle things like right-censoring, seasonality, re-installs, bots, and timezone edge cases?
  • If a PM wants to run an A/B test to improve retention, how would you design and evaluate it, including randomization unit, experiment duration, delayed effects, and variance reduction?

“I started with N-day retention because it felt safest, cohort of users who signed up on day 0, check if they came back on exactly day N.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for a software engineer role. Pretty standard stuff with one twist at the end: they asked about my experience using AI tools, which I wasn't really expecting in a BQ context.
  • What project are you most proud of and why?
  • Tell me about a time you had to resolve a conflict with a teammate or colleague.
  • What has your experience been with using AI tools in your work?

“Went fine.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta system design round focused entirely on post privacy, the kind of problem that sounds scoped until you start pulling on the caching thread and realize how many edge cases are hiding in there. Solid problem, made me think harder than expected.
  • Design a privacy system for social media posts that supports multiple visibility levels (public, friends, friends-of-friends, only me, and custom allowlist/denylist/groups), enforces those rules on both feed reads and direct URL access, and handles high read throughput with caching while staying consistent when friendships or privacy settings change.

“I started with the data model, which felt natural, but I spent too long on the schema and not enough time on the enforcement layer.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, one question on vertical traversal of a binary tree. Pretty standard algorithmic problem but it has enough edge cases to trip you up if you haven't seen it before.
  • Given a binary tree, return the values of nodes grouped by their vertical column, ordered top to bottom and left to right within each column.

“The core idea clicked fast for me, BFS with coordinate tracking.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a product design role at Meta and got hit with a classic product sense question about Yelp. Pretty standard for this type of loop but still requires some prep to not fumble it.
  • What product or feature would you add to Yelp to improve it?

“I went with a feature around personalized recommendations based on past dining behavior, which felt solid but in hindsight I spent too long on the setup and not enough time actually defending the prioritization.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta system design coding question, fourth level of a multi-part assessment. The problem built on previous levels and added grant periods with some tricky containment logic. Felt manageable but the edge cases on partial overlaps took longer than I expected.
  • Extend a work hours tracking system to support bonus salary periods: implement register_grant(start, end) to record double-pay windows, upgrade calc_salary so a session only gets doubled if it falls entirely within a registered grant period (partial overlap doesn't count), and implement get_grant_bonus(start, end) that returns the extra earnings tied to an exact previously registered grant.

“The containment rule is where I lost time.”

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