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Meta interview notes, page 60 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

May 2026
Meta SWE interview with a classic palindrome check question. Not much to report, pretty standard coding screen.
  • Determine whether a given string is a valid palindrome.

“Pretty bread and butter as far as coding questions go.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a meaty product analytics case built around group call quality and capacity decisions. One question, but it had four sub-parts that each could've been their own conversation. Felt more like a product strategy exercise than a stats exam, which I wasn't fully expecting.
  • You're deciding whether to cap the maximum number of participants in a Group Call. Given 28 days of historical group size data and a quality-of-service model where call quality degrades linearly with group size, determine: (a) how to visualize the size distribution with properly labeled axes, (b) the 95th percentile of that size distribution, (c) whether that percentile satisfies a minimum call quality threshold and what cap to set if it doesn't, and (d) the trade-offs between quality and reach, plus an adaptive capping policy and a statistical rule for monitoring the cap over time.

“This one took me a second to parse because the four parts feel deceptively independent but they're really a chain.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design round focused entirely on building a real-time auction feature for a large photo and video platform. Pretty deep dive, covered a lot of ground from data modeling to concurrency control to scaling hot celebrity auctions.
  • Design a real-time auction system for a large social photo and video app, where creators can attach auctions to posts and all viewers see the current highest bid with low latency.
  • How would you handle bid ordering and concurrency control to ensure correctness when many users bid simultaneously?
  • How would you push real-time highest-bid updates to all viewers watching an auction with low latency?
  • How would you scale the system specifically for celebrity auctions where bid volume spikes dramatically compared to normal auctions?
  • Walk through your API design for the auction feature, including how clients place bids and retrieve auction state.
  • How would you handle failure scenarios, such as a bid being lost in transit or the auction service going down mid-auction?

“This one is bigger than it looks.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist screen with a Bayesian probability question. Pretty standard stuff if you've seen it before, but the numbers are set up to trip you up if you just go on instinct.
  • A classifier flags potentially bad users. 5% of users are actually bad, and the model correctly identifies users in both classes 95% of the time. If the model predicts someone is bad, what's the actual probability they're truly bad?

“The 95% accuracy sounds reassuring until you run the numbers and realize the base rate is doing most of the damage.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta phone screen for a SWE role, centered entirely on palindrome problems. Classic Meta coding territory, and the follow-up extension to K deletions is where things get real.
  • Given a string, return whether it can be made into a palindrome by deleting at most one character.
  • Follow-up: what if you can delete at most K characters instead of just one? How does your solution change?

“Two-pointer skipping once on mismatch is the move.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a meaty SQL case study involving two CSVs, schema design, and multi-step aggregation. The problem was technical enough that it felt more like a take-home than a phone screen, covering visit-level and visitor-level conversion logic with some genuinely tricky edge cases around booking attribution and deduplication.
  • Given two tables, visits and bookings, write SQL to produce a visit-level dataset where each row has a booked_flag indicating whether a booking occurred after that visit but before the next visit or 28 days later, whichever comes first. Make sure a single booking isn't counted across multiple visits for the same visitor.
  • Now produce a visitor-level dataset with one row per visitor, using their first visit timestamp and assignment. Set booked_flag_28d to 1 if any booking falls within 28 days of that first visit. If a visitor has conflicting assignment values across visits, use the one from the earliest visit.
  • For both datasets, aggregate conversion metrics by assignment group and country: total visits or visitors, number of bookers, and conversion rate. Be explicit about how you handle duplicates and timezone assumptions.

“The dedup constraint is what makes this hard.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta with one question about measuring AI success. Pretty thin on details but the question itself is a meaty one worth thinking through.
  • How would you measure the success of an AI feature rollout at a large streaming platform like Netflix?

“I went straight for engagement metrics and kind of forgot to anchor on what problem the AI was actually solving first.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a product design question about wine drinkers. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product targeted at wine drinkers.

“I went straight into features and the interviewer just kind of waited.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round, got a tree problem that looks easy on the surface but has a subtle recursive structure that trips people up if they haven't seen it before.
  • Given a binary tree, implement a function that returns the diameter, defined as the length of the longest path between any two nodes in the tree.

“The tricky part is that the longest path doesn't have to go through the root, which is what kills most people.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

May 2026
Product Analyst loop at Meta focused entirely on WhatsApp group video calling. Six questions, all interconnected, and the experiment design ones were trickier than I expected going in.
  • How would you figure out whether users actually need or care about group video calling?
  • What product changes could you make to increase the number of participants joining a group video call?
  • Walk me through how you'd design an A/B test for a change meant to increase group video call participation.
  • What metrics would you track as success metrics, guardrails, and diagnostics for this experiment?
  • What trade-offs might exist between the metrics you're tracking?
  • How do network effects in a messaging product affect how you'd run and interpret this experiment?

“I went straight to usage data and surveys, which felt a bit flat in hindsight.”

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