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Meta interview notes, page 127 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·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta product interview, one question about growing Instagram engagement. Pretty sparse on details but the question itself is a classic.
  • How would you increase engagement on Instagram?

“Broad enough that you can go a dozen directions and still feel lost.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta SWE system design round, got asked to build a music player using MVC. Pretty standard architecture question but I fumbled some of the component boundaries more than I expected.
  • Design a music player application using the MVC (Model-View-Controller) architecture.

“I started with the Model layer and listed out the obvious stuff: tracks, playlists, playback state.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product strategy interview at Meta for a PM role, centered on a single meaty prompt about applying generative AI to enterprise business messaging. The whole session was basically one long case with follow-ups branching off it.
  • Explain what generative AI is and how you would apply it to an enterprise business-messaging product. Pick one industry segment, identify the most valuable use cases for that segment, and describe how you would measure success.
  • Of the use cases you identified, which one would you launch first and why?
  • How would you prevent the model from generating hallucinated or non-compliant responses?
  • How would you price or package this feature for enterprise customers?
  • How would your approach change if you were building this for a heavily regulated industry like financial services or healthcare?
  • Automation improves your cost metrics but customer trust scores start dropping. What do you do?

“This is the core prompt and it's deceptively wide.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta interview, one of those project deep-dives where they really want to peel back every layer of your decisions. The whole thing felt less like a conversation and more like a technical audit of your past work.
  • Walk me through a recent project: what was the problem, what was your role, what technical decisions did you make and why, and what was the outcome? Be ready for follow-up questions on your design choices.

“This sounds like a softball but it absolutely is not.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Interviewed at Meta for what looked like a data or analytics role, got a probability question about coin flips that felt deceptively simple but had some wrinkles worth thinking through.
  • Given a fair coin, predict the distribution of outcomes after a series of flips. Walk through your reasoning.

“Seemed straightforward at first and I almost rushed through it.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE screen, basically just a single question about my JavaScript background. Short and uneventful, not much to report.
  • Walk me through your experience with JavaScript.

“Pretty open-ended.”

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

May 2026
Meta MLE loop for ads ranking. The focus was heavily on feature engineering and calibration rather than architecture, which surprised me a bit. One round went almost entirely into data pipeline and logging, barely touched the model itself.
  • Design an ads ranking system: how would you select and rank ads for a given user impression slot?
  • Walk me through how you'd handle model calibration in an ads system, and why it matters here specifically.
  • What features would you use for ads ranking, and how would you organize them?
  • How would you source, log, and label training data for the ads ranker?
  • What metrics would you use to evaluate the ads ranking system, and how do you balance advertiser value against user experience?

“This is the core question and it goes way deeper than you'd expect.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Went through a Meta interview that touched on ML fundamentals. Just the one question from what I can tell, but it's the kind of thing that sounds basic until you actually have to explain it clearly under pressure.
  • Why does feature scaling and normalization matter, and when would you apply it?

“I knew the answer but fumbled the delivery.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round with a maze pathfinding problem. Pretty standard BFS territory but the I/O format tripped me up more than the actual algorithm did.
  • Given an n x m grid where 0 is a walkable cell and 1 is a wall, find the shortest path between two boundary points and output the coordinates along that path. If no path exists, output 'No Path'.

“BFS was the obvious move and I knew that immediately, but I fumbled around with how to reconstruct the actual path coordinates instead of just returning a boolean or a length.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a SWE role at Meta, just one question about React experience. Pretty thin on details but it felt like a standard technical screen warmup.
  • Walk me through your experience working with React.

“Pretty open-ended so I just started rambling about component lifecycles and hooks.”

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