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Meta interview notes, page 214 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·Technical Program Manager·Hiring Manager Screen

Jul 2026
Interviewed for a TPM role at Meta, just one question about how I actually run programs day to day. Short but surprisingly hard to answer well on the spot.
  • How do you typically approach executing a program from start to finish?

“Blanked for a second because it sounds so open-ended.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta coding interview with just one problem, finding the smallest missing integer. Pretty standard on the surface but worth thinking through the edge cases carefully.
  • Given an array of integers, find the smallest missing positive integer.

“I jumped straight to sorting and scanning, which works but they pushed back on the space complexity.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE interview with a tricky algorithmic problem that pushed me to think beyond linear time. The question felt deceptively simple at first glance but the sub-O(n) constraint is what made it actually hard.
  • You're given a sorted array with lots of duplicates but very few distinct values. Count the number of unique values using fewer than O(n) operations. How would you approach this, and what's the complexity?

“I started with the naive scan and immediately caught myself because they literally said sub-linear.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Meta system design round focused entirely on a real-time leaderboard for an online game. The scope was well-defined but there were enough moving parts (global vs regional queries, rank neighbors, anti-cheat) that it kept me busy the whole session.
  • Design a real-time leaderboard system for an online game where players submit score updates frequently, and the system needs to support querying the top N players both globally and by region, as well as a player's current rank and nearby neighbors, all with low latency.

“I went straight to Redis sorted sets and they seemed fine with that direction, but I fumbled a bit when they pushed on regional breakdowns.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Stats-heavy technical screen for a Data Scientist role at Meta. Two probability/hypothesis testing questions, both centered on LLM output quality evaluation. Pretty niche framing but the underlying math is standard.
  • A chatbot produces a good response with probability x. If the first three responses were all good, what is the probability the fourth response is also good?
  • One LLM has a 70% good response rate and another has 80%. Run a hypothesis test to determine whether this difference is statistically significant, and explain your result.

“The key is the independence assumption, which they tell you to use.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
PM strategy question for a Meta role, framed as a Netflix scenario. Just the one question but it had a lot of layers to it.
  • You're a PM at Netflix. Should Netflix launch a podcasting app?

“I went straight into market sizing and competitor analysis, which felt safe but probably wasn't the move.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta data scientist interview with a probability/Bayes question that spiraled into five parts. The core setup was straightforward but the later parts tripped me up more than I expected.
  • Given a pool of reviewers where 20% are lazy (always give good reviews) and 80% are careful (give good reviews 60% of the time), what is the probability that a randomly selected review is good?
  • If a review turns out to be negative, what is the probability it came from a lazy reviewer?
  • What is the expected number of good reviews out of 100 total reviews?
  • A reviewer gives three consecutive good reviews. What is the probability that reviewer is lazy?
  • How does the probability that a reviewer is lazy change as the number of consecutive good reviews grows toward infinity?

“This part was fine.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE coding round, one tree problem the whole time. Pretty standard stuff if you've done your BFS prep, but I fumbled around longer than I should have before landing on the right approach.
  • Given the root of a binary tree, write a function to determine whether the tree is a complete binary tree. Return true if it is, false otherwise.

“Spent probably two minutes just restating the definition back to myself out loud which felt awkward.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS technical screen, two SQL problems back to back. Both were about querying a social platform's post and user tables, and the second one had a tricky percentage calculation that I almost set up wrong.
  • Given a posts table and a users table on a social platform, count the number of distinct users who authored at least one post that received 2 or more replies within 7 days of that post being created.
  • For users who received at least one reply to any of their posts, what percentage received replies from at least 2 distinct US-based users?

“I self-joined the posts table to pair parent posts with their replies, then filtered on the timestamp window.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Meta for a software engineer role. The problem was designing an online auction platform end to end, covering everything from data modeling to real-time bid broadcasting to consistency guarantees. Pretty dense question with a lot of moving parts.
  • Design a scalable online auction platform where users can list items and place bids in real time. Cover the high-level architecture, data model, real-time updates, consistency, scalability, and fault tolerance.
  • How would you handle real-time bid updates to clients watching an auction? Compare polling, long polling, and Server-Sent Events, and explain your choice.
  • How do you guarantee bid consistency so that no two users are declared winners and no valid winning bid is lost, especially under high concurrency near auction end?
  • How would you prevent or mitigate last-second bid sniping, and how would you handle network delays and retries when a bid is submitted?
  • Walk through the full flow for closing an auction and determining the winner.

“This one sprawled in every direction.”

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