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Meta interview notes, page 199 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Meta ML engineer interview that was basically one meaty coding and design question about friend recommendations. The AI-assistance angle was a nice twist I wasn't expecting, felt more like a real work session than a whiteboard grind.
  • Design and implement a Top-K friend recommendation system using mutual friends as the ranking signal. For a given user, score each non-friend candidate by the number of friends they share, then return the K highest-scoring candidates. Walk through your implementation using the existing User class, and discuss time and memory complexity.
  • You're allowed to use AI assistance here. Ask it to propose alternative ranking signals beyond mutual friends, such as Adamic-Adar or Jaccard similarity, then analyze the trade-offs between them.

“I went straight to set intersection and it felt clean enough.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a weighted random sampling problem that seems straightforward until they ask the follow-up. The prefix-sum solution gets you partway there, but the real test is whether you know the Alias Method for high-throughput scenarios.
  • Implement a class that samples a random index proportional to a given list of weights, where pickIndex() should return index i with probability w[i] divided by the total weight sum.
  • Your pickIndex() is being called millions of times in production. How would you optimize it beyond the binary search approach?

“The prefix-sum plus binary search solution is fine and I had it working in maybe four minutes.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product strategy question about the dating feature. Pretty conceptual, no technical stuff, just pure 'why does this exist and how do you know it's working' territory.
  • Why do you think Meta built a dating product, and how would you measure its overall success including a North Star metric?

“Two questions stitched together and I kind of fumbled the seam between them.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview with a deep stats question about modeling user behavior distributions. Single question but it had four layered parts and felt more like a take-home problem squeezed into a live session. Pretty intense for what I expected.
  • You have a new user cohort. Model the distribution of P2P transfer counts per user in the first 30 days after signup: justify your choice of distributional family including zero-mass and heavy-tail behavior, sketch where mode, median, mean, and 95th percentile fall relative to each other and why, predict how the distribution shifts by day 60 given retention effects and fraud suppression, and recommend two executive-level summary statistics robust to heavy tails plus one diagnostic to validate your assumptions.

“Four sub-questions in one.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, pretty standard list filtering problem. Nothing too wild but the set-based optimization is the kind of thing they expect you to just know.
  • Given two lists of integers, return all elements from the first list that do not appear in the second list, maintaining the original order.

“Pretty clean problem on paper.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
SQL-heavy Data Scientist screen at Meta, three questions all built around the same four-table bookstore schema. The problems escalated pretty quickly from aggregation basics to some genuinely tricky referral chain logic.
  • Using a four-table bookstore schema (books, authors, transactions, customers), write a query to return the top 5 payment methods ranked by total sales amount.
  • Calculate two proportions: (a) what fraction of authors have zero sales, and (b) what fraction of authors have a personal URL containing a specific keyword string.
  • Using the customers table where some customers have a referring customer ID, find the 5 referrers whose referred customers bought books at the highest average price. Exclude customers who weren't referred by anyone.

“Straightforward aggregation, sum the amount column from transactions, group by pay_method, order descending, limit 5.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round, one big question about building a price tracking and alerting service at scale. The kind of question where you think you know where to start and then realize there are like six different hard sub-problems hiding inside it.
  • Design a price tracking service similar to CamelCamelCamel that crawls Amazon product prices, stores historical price data, lets users view price history and all-time lows, and sends alerts when a product hits a user-defined target price. Cover data modeling, ingestion pipeline, storage, alert evaluation, and overall architecture.

“I started with the data model which felt safe, but then the ingestion side caught me off guard.”

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Meta·Technical Program Manager·Technical Phone Screen

Jun 2026
TPM interview at Meta with a metrics design question for a voting system. Short and focused, but don't let the simplicity fool you.
  • What metrics would you use to measure the health and success of a voting system?

“I went straight to engagement numbers and kind of forgot about integrity metrics entirely for the first few minutes.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta data scientist interview, one technical round focused entirely on model evaluation for a fake account detection system. Pretty conceptual but they pushed hard on the business side of things, not just the stats.
  • You've built a classifier to detect fake accounts and leadership wants proof it's working in production. Which evaluation metrics would you use and why? Walk through the trade-offs between precision, recall, F1, and ROC-AUC, and factor in the business costs of false positives versus false negatives.

“I started with the usual precision vs recall framing and talked about class imbalance since fake accounts are a small fraction of all accounts.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, got a custom alphabet sorting problem. Pretty clean problem once you see the trick, but I fumbled around longer than I should have before the approach clicked.
  • Given a custom alphabet ordering and a list of words, determine whether the words are sorted in lexicographic order according to that custom alphabet rather than the standard English one.

“The key move is building a rank lookup from the order string first, then just comparing adjacent word pairs.”

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