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Meta interview notes, page 135 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 software engineer interview with a coding round focused on building a small banking system from scratch. Pretty straightforward premise but the details matter more than you'd expect.
  • Design and implement a basic banking system that supports deposit and withdrawal operations on user accounts, where withdrawals that would result in a negative balance should be rejected.

“Felt easy at first glance and that's kind of the danger.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a single product design question about a grocery delivery app. Pretty open-ended, which sounds chill until you're sitting there trying to figure out where to even start.
  • Design a mobile app for grocery delivery.

“I started with users and worked outward, which felt right in the moment, but I spent way too long on personas and the interviewer had to nudge me toward actual features.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta software engineering interview with two algorithm-heavy problems. Nothing too exotic but the second one had some depth to it that I wasn't fully prepared for.
  • You're given two sorted arrays. Merge them into one sorted array. The interviewer wants both an in-place version (assuming the first array has extra capacity at the end) and a version that uses an auxiliary buffer. Walk through time and space complexity for each, and say something about stability.
  • Given a binary tree, flatten it in-place into a linked list in preorder order, using the right pointers as the 'next' links and setting all left pointers to null. Describe the algorithm, argue why it's correct, and give the complexity.

“I knew this one.”

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

May 2026
Brutal system design loop at Meta for an MLE role. The whole thing was basically one massive open-ended question about image generation that kept branching into multimodal territory, and they expected you to go deep on both the research side and the production side.
  • Design an end-to-end image generation system, covering data collection and curation, model architecture, training objectives, compute planning, safety filtering, evaluation, and inference optimization.
  • Extend your image generation design to support multimodal input and output, where the system can both accept and produce text and images together.
  • Walk through a specific recent paper relevant to your design: explain the key idea, experimental setup, metrics used, trade-offs, and how you would adapt it for production.

“This question is enormous and I think I underestimated that at first.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta data science interview with a product strategy question about whether to build out a group video calling feature. The question was framed around using existing 1:1 call log data to make the case, which pushed it more into analytics territory than I expected.
  • Should Facebook launch a group video calling feature? Walk through how you'd use existing 1:1 call log data to inform that decision.

“The product strategy part felt fine but I got a bit tangled when they pushed on what signals in the call logs would actually tell you anything useful about group demand.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a PM role at Meta. Just one question but it took up the whole conversation, lots of follow-ups digging into the specifics of what I gave up and why.
  • Walk me through a time you had to make a prioritization tradeoff.

“I went in thinking this was a softball and kind of rambled through a story about deprioritizing a feature to hit a launch date.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE interview with a tricky algorithmic twist. The classic 'find the unique element' problem but with an O(log n) constraint, which changes everything about how you approach it.
  • You have an array where every number appears exactly twice except for one. Find the unique number in O(log n) time.

“My first instinct was XOR, which gets you there in O(n).”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding round where the actual algorithm was the easy part. The whole challenge was just figuring out how to use their weird nested class structure to get the data you needed in the first place.
  • You're handed a codebase where a maze is wrapped in deeply nested OOP classes. The grid, walls, start, and end cells are only reachable through chained method calls on these objects. Implement BFS to find the shortest path from start to end.

“BFS itself took maybe five minutes to write.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Short Meta interview loop, just one question about model freshness. Not much context given so hard to say how it went.
  • How do you determine when a machine learning model needs to be retrained or refreshed?

“I went with the obvious signals first, like performance degradation on held-out data, then talked about data drift and concept drift.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a product sense question about the Facebook Birthdays feature. Pretty focused session, just the one question but it went deeper than I expected.
  • How would you improve the Facebook Birthdays feature?

“Seems like a softball until you're actually in it.”

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