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Meta interview notes, page 117 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·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a Data Scientist role at Meta, all about team dynamics and how you handle the messy human side of working with others. Nothing technically deep, but the questions were pointed enough that vague answers probably wouldn't fly.
  • Tell me about a project you delivered that required a major pivot. What was your specific role and what impact did you have?
  • If a teammate's contributions aren't adding much value to a project, would you still include them? How do you think about that decision?
  • Describe a time you received constructive feedback. How did you respond in the moment and what did you actually change afterward?
  • Give an example of a time you had to pick up a new skill quickly. What was the situation and what came out of it?
  • Tell me about something you did poorly. What did you do to compensate or recover from it?
  • How do you manage your working relationships with colleagues, particularly when there's friction or disagreement?

“I had a decent story ready but kept second-guessing whether the pivot was dramatic enough to count.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
A Meta DS interview built entirely around fake account detection on a social platform, starting from zero: no model, no rules, no metrics. The whole thing was one long case that kept branching, and I spent most of it feeling like I was one follow-up away from falling apart.
  • How would you operationally define a 'fake account' for a social platform, and how do you handle gray-area cases?
  • What events and data fields would you log for friend requests and downstream user actions, and how would you design the schema to track outcomes over time?
  • Without any existing model, what heuristic signals or risk scoring approach would you build first to detect fake accounts using only friend request data?
  • How would you obtain labels for fake accounts, and how do you deal with the fact that labels are delayed, sparse, or biased toward certain types of fakes?
  • What are the primary, diagnostic, and guardrail metrics for a fake account detection system?
  • How would you estimate and report the platform-level prevalence of fake accounts over time when you only have partial ground truth, and how does your reporting differ for executives versus an operational team?

“I started rattling off categories: spam bots, scam accounts, coordinated inauthentic behavior.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a software engineer role. Just one question but they kept probing, so it ended up being a longer conversation than I expected.
  • Tell me about a time you stepped up to volunteer for an important project.

“They weren't really satisfied with my first answer and kept pushing on why I chose to get involved and what I did to move things forward.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

May 2026
Meta DS interview focused on product analytics for Facebook and Instagram. Two meaty case questions back to back, both requiring funnel thinking and metric trade-off reasoning. Felt like a product sense round wearing a data science costume.
  • How would you increase the number of posts in Facebook Groups that receive at least one comment?
  • What changes would you propose to increase in-app purchases on Instagram?
  • After launching a product change, you notice a key metric has dropped. How do you diagnose what happened and decide whether the trade-off is acceptable?

“I started with a funnel breakdown (who sees a post, who reads it, who actually types something) and that structure held up fine.”

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Meta·Technical Program Manager·Onsite - Behavioral / Leadership

May 2026
Interviewed at Meta for a TPM role and got hit with a classic influence-without-authority question. Not a lot of context to go on, but it felt like a behavioral round focused on leadership and org dynamics.
  • Describe a time you shaped or redirected a team's roadmap and changed how they were thinking about a problem.

“I blanked for a second trying to pick the right story.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a product sense question framed around a children's museum scenario. Pretty classic Meta-style case but the fictional context threw me off more than I expected.
  • You're the head of product at a children's museum and engagement is down. How do you approach fixing it?

“The museum framing is clearly just a wrapper to see if you can diagnose a metrics drop and prioritize solutions without a tech product safety net.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding screen for a software engineer role, just the one problem but it had enough edge cases to keep things interesting.
  • Implement a power function that computes x raised to the exponent n, handling negative exponents and doing it efficiently for large values of n.

“Knew the fast exponentiation approach going in, so I wasn't panicking.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding screen for a software engineer role, focused entirely on a BST range sum problem. Pretty standard algorithmic question but they pushed hard on complexity analysis and wanted both recursive and iterative implementations, which tripped me up a bit.
  • Given the root of a binary search tree and two integer bounds (inclusive), return the sum of all node values that fall within that range. Implement both a recursive and an iterative solution, use BST properties to prune unnecessary branches, and walk through the time and space complexity of each approach.

“The recursive version came naturally, prune left if the current node is below the low bound, prune right if it's above the high, otherwise recurse both sides and accumulate.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design screen, just the one question about building a typeahead for a search engine. Pretty focused session, no fluff.
  • Design a typeahead (autocomplete) system for a search engine.

“I jumped straight into the trie data structure and the interviewer kind of let me run with it before asking about scale.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a Data Scientist role, all cross-functional collaboration territory. Four questions, pretty standard stuff, but the trust-building one caught me off guard a bit.
  • Tell me about a time you gave constructive feedback that led to a positive outcome.
  • Describe a situation where you disagreed with a teammate and how you resolved it.
  • What was the biggest obstacle you faced on a project and how did you get past it?
  • How did you quickly build trust with stakeholders when joining a new team?

“I had a decent story ready but fumbled the result part.”

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