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Meta interview notes, page 9 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·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at Meta, just the one question from what I can tell. Not much to go on but it's a classic opener they seem to like.
  • Why do you want to work as a PM at Meta specifically?

“Pretty standard motivation question but it's easy to fumble if you haven't thought about why Meta versus any other big tech company.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, one question about sorting a nearly-sorted array. Pretty focused session, no fluff.
  • Given an array where every element is at most K positions away from its correct sorted position, sort the array efficiently.

“Classic heap problem once you see it, but I stared at it for a bit before the min-heap idea clicked.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta product sense question about improving the travel experience for Facebook employees. Pretty open-ended, not a lot of context given upfront.
  • How would you improve the travel experience for Facebook employees?

“This one tripped me up a bit because it's internal-facing, not a consumer product.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Meta software engineer behavioral round, pretty standard stuff with the usual questions plus a couple about AI usage and personal reflections. Nothing too wild but the AI angle was a bit unexpected.
  • Standard behavioral questions about your past work and experience.
  • How do you use AI tools in your day-to-day engineering work?
  • What are your key takeaways or reflections from your recent projects or career so far?

“Ran through the usual stuff, situation-action-result format for most of it.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta MLE technical screen focused on a coding problem in a friend recommendation system. Pretty standard setup but the follow-up discussion on scaling was where things got interesting.
  • Given a User class with a friend list and a valid_recommend function, implement random_recommend(user) that returns a uniformly random valid candidate, or None if no valid candidate exists.

“The naive path is obvious: filter all users through valid_recommend, throw them in a list, pick randomly.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for a SWE role, pretty standard stuff. One question about feedback, which sounds easy until you actually have to come up with a good example on the spot.
  • Tell me about a time you delivered important feedback to someone.

“I had an example ready but it felt thin once I started talking through it.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview with a product design question focused on internal tooling. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product that helps Facebook employees improve their learning and development.

“I went straight to features and probably spent too long there before anchoring on who the actual users were.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for a software engineering role. Just one question on conflict resolution, pretty short session from the looks of it.
  • Tell me about a time you had a conflict with a teammate or colleague and how you handled it.

“Pretty standard behavioral territory.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta machine learning interview, one technical question about gradient issues in neural nets. Pretty focused session, no fluff.
  • If you're training a neural network and gradients start exploding, what do you do about it?

“I knew the textbook answers: gradient clipping, lower learning rate, maybe batch norm.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Interviewed at Meta for a data science or analytics role, got hit with an experimentation question that seemed straightforward but had more layers than I expected.
  • How do you detect novelty effects or primacy effects in an experiment?

“I knew the concepts but fumbled a bit explaining how to actually distinguish between the two in practice.”

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