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Meta interview notes, page 159 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 - Behavioral / Leadership

Jun 2026
Went through a product design interview at Meta, behavioral focus, one question about process improvement. Pretty standard stuff but it made me realize how much I overthink these situational prompts.
  • Tell me about a time you improved a process to make your team more productive.

“I had an answer ready but rambled too much setting up the context and barely got to the actual impact before time ran out.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Two ML system design questions at Meta, both pretty meaty. The kind of round where you realize halfway through that you probably should have reviewed recommendation systems more recently.
  • Design a real-time recommendation system that suggests nearby places to a mobile user whose location can change while the app is running.
  • Design a ranking and filtering system for notifications coming from multiple sources, including how to decide which ones to send, which to suppress, and how to order them.

“The moving user part is what gets you.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, pretty much a classic selection-order problem. Nothing too wild but the follow-up on complexity tripped me up a bit.
  • Given an integer array and an integer k, find and return the k-th largest element in the array.

“I jumped straight to sorting which they accepted but then asked about the expected linear time approach.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
Meta DS interview centered on a product case about Instagram's account switcher feature. Four questions covering identity detection, experiment interpretation, launch decisions, and user segmentation. Pretty technical for a product analytics round but not impossible.
  • How would you go about detecting that multiple Instagram accounts belong to the same real person?
  • An experiment on the account switcher shows users are managing more accounts but total time spent in the app hasn't changed. What's going on, and what data would you look at?
  • Based on what you know, would you recommend Meta fully launch the account switcher feature?
  • Which user segments might actually be unhappy with the account switcher, and why?

“I started with device fingerprinting and IP overlap, which felt obvious, but then got a bit lost on how to weight signals when they conflict.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Meta for a Data Scientist role, all situational questions centered on teamwork and adaptability. Nothing too surprising but a few of them required more specific examples than I had ready.
  • Tell me about a time you had to pivot a project quickly. What happened and how did you handle it?
  • Describe a situation where you gave constructive feedback to a teammate. How did you approach it?
  • Give an example of a time you persuaded others to adopt your idea or approach.
  • A colleague feels unwelcome or excluded on the team. What would you do?

“I had a decent story for this but fumbled the outcome part.”

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Meta·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta coding round for an MLE role. One question, but it had layers. The core was a grid traversal problem with a twist that took me a minute to fully internalize.
  • Given a grid with walls, a start, an end, and some cells marked as bombs, find the shortest path from start to end. Stepping on a bomb destroys all wall cells within a certain Manhattan radius, making them walkable for the rest of the search. Each bomb can only be triggered once.

“My first instinct was plain BFS and I started coding before fully thinking through the state space.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Meta software engineer interview, behavioral/leadership round focused entirely on a single deep project walkthrough. The question had a lot of surface area and I felt like I was rambling by the end.
  • Walk me through a recent project you led: what was the problem, what were your responsibilities, what technical decisions did you make and why, what were the measurable outcomes, and what would you do differently? Also cover how you worked across teams and dealt with setbacks or shifting requirements.

“This question is basically five questions stapled together and I did not handle that well.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a single AI product design question for a ride-sharing app. Pretty open-ended, which sounds freeing until you're actually sitting there trying to figure out where to start.
  • Design an AI-powered feature for a ride-sharing app. Who are you building it for, what exactly are you building, and why?

“The question felt broad enough that I spent probably too long just picking a user segment before committing.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, basically a banking/transaction system design problem that kept getting extended with follow-ups. The core idea was straightforward but the scaling requirements made it more interesting than I expected.
  • Design a system that tracks each user's total transaction volume (sum of absolute amounts across all successful deposits and withdrawals) and returns the top K users by that metric, with ties broken by user ID.

“The hashmap part was fine, I had that pretty quick.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Brutal onsite-style question for a DS role at Meta. The whole thing was one massive experiment design problem set around a travel app push notification test, and it covered basically every angle of A/B testing you can think of, from metric selection to spillover to sequential monitoring. Dense and formula-heavy.
  • What should be the primary success metric for a push notification experiment on a travel app, and how do you justify it over alternatives like booking rate or content views? Define the exact numerator, denominator, and attribution window.
  • Which guardrail metrics would you monitor for this experiment, what alert thresholds would you set, and how do you handle sequential monitoring to avoid inflated Type I error?
  • What should the randomization unit be for this experiment? Walk through spillover pathways and the tradeoffs of larger versus smaller clusters, unequal cluster sizes, and cross-border travelers.
  • How do you power a cluster-randomized experiment? Define intracluster correlation and design effect, and show with numbers how required sample size changes compared to individual randomization.
  • Describe your randomization and stratification plan for this experiment, including how you would validate covariate balance at both the user and cluster levels.
  • What analysis approach would you use: cluster-level difference-in-means, user-level models with cluster-robust standard errors, or mixed-effects models? How do you handle partial exposure and users who move between clusters?
  • How would you detect and quantify spillover effects in this experiment? How does that change your estimand and analysis?
  • What operational safeguards would you put in place for this experiment, including staged rollout, early-stop criteria for guardrail breaches, and what you monitor in the first 48 hours?

“I went with 7-day retained sessions per user and spent a while defending it.”

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