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Meta interview notes, page 171 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

Jun 2026
Did a coding screen at Meta, got the classic stock price problem. Pretty standard stuff but worth knowing cold if you're interviewing there.
  • Given an array of daily stock prices, find the maximum profit you can make by buying on one day and selling on a later day. Return 0 if no profit is possible.

“Classic single-pass problem.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round, one question the whole time. Pretty stripped down, no warmup, just straight into it.
  • Design a DNS cache.

“I started with the basic TTL-based eviction and a hash map structure, which felt right, but then they pushed on consistency across distributed nodes and I kind of fumbled.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one problem the whole session. Grid BFS with keys and doors. Felt manageable once I figured out the state representation, but getting there took longer than I'd like to admit.
  • Given a 2D grid with a start cell, an exit cell, walls, empty cells, keys (a-f), and doors (A-F) that require the matching key to pass through, find the minimum number of steps to get from start to exit moving in four directions. Return -1 if it's not reachable.

“The first thing I tried was plain BFS and the interviewer just kind of waited.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview that went deep into testing methodology for a maze-solving controller. The whole session was essentially one big problem broken into a lot of sub-cases, which I wasn't fully expecting.
  • Design a comprehensive unit test suite for a mouse-maze controller, covering edge cases like single-cell mazes, unreachable cheese, narrow corridors, cul-de-sacs, loops, large open spaces, repeated visits, backtracking correctness, multiple cheeses, performance limits, and API error behavior.
  • How would you stub or simulate the maze interface deterministically so your unit tests don't depend on a real implementation?
  • How do you test that your maze controller correctly returns the first cheese found rather than any other cheese in a multi-cheese maze?
  • How would you write a test to verify the controller handles a maze with cycles without getting stuck in an infinite loop?

“This was the core question and it took a while to realize they wanted me to think about ALL the edge cases systematically, not just write a few happy-path tests.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a BST range sum problem that seemed straightforward until the follow-up hit. The optimization discussion went deep fast and I wasn't fully prepared for the trade-off analysis.
  • Given the root of a binary search tree and two integers representing a range, return the sum of all node values that fall within that inclusive range.
  • If the tree is very large and the range query changes frequently, how would you optimize it? Walk through augmenting nodes with subtree sums, precomputing an in-order array with prefix sums for binary search lookups, and the trade-offs between those approaches for read-heavy versus write-heavy workloads.

“The base problem wasn't bad.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round with a dictionary-focused problem that had two parts. Pretty straightforward on the surface but the prefix matching piece required some thought.
  • Write two functions: one that scans a dictionary for records with an exact key match, and another that scans for records whose key starts with a given prefix.

“The exact match part was fine, nothing to it.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Got a system design round at Meta for an EM role and the question was just 'Design Twitter.' Classic.
  • Design Twitter.

“Wide open question and I spent the first few minutes just trying to scope it down because 'Twitter' is basically everything.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta product sense interview, one question about Facebook Groups. Pretty open-ended and I felt like I was rambling for a bit before finding my footing.
  • How would you improve Facebook Groups to increase the sense of community among members?

“I started by trying to define what 'community feeling' actually means, which felt smart in the moment but probably ate up too much time.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product sense question framed around Netflix. One question, fairly open-ended, the kind where you can go in a lot of directions and that's both the appeal and the problem.
  • You're the PM for Netflix's homepage. How do you define goals for it, and what metrics would you use to track success?

“I went straight to engagement metrics like time spent and click-through rate, which in hindsight felt a bit surface-level.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
SQL-heavy technical screen for a DS role at Meta, focused entirely on Ads Pixel analytics. Two back-to-back query problems, both requiring you to think through weighted averages and bucketing logic under the hood. No behavioral, no product sense, just write the SQL and explain your choices.
  • Given tables for advertisers, pixels, and daily pixel signal data, write a SQL query returning one row per advertiser per day showing: number of active pixels (those with at least one event), total events received, and an event-weighted average valid event rate across active pixels.
  • Extend the previous query to study how pixel signal quality relates to ad performance. Define a per-advertiser daily quality score (event-weighted average of valid_event_rate times match_rate), bucket each advertiser-day into low, medium, or high quality tiers, then aggregate spend, clicks, conversions, CVR, and ROAS by bucket across the 30-day window.

“The active pixel filter tripped me up a bit.”

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