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Meta interview notes, page 179 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·Technical Phone Screen

Jun 2026
Two-part Meta DS interview focused entirely on feed personalization. Part A was a deep causal inference design problem, Part B was a full A/B experiment spec. No behavioral questions, no SQL, just two very meaty open-ended cases back to back.
  • Using impression-level and interaction-level logs plus a friendships graph, design an analysis to validate whether friends' content drives more social engagement than unconnected content in a personalized feed. Cover how you'd define a 'socialness' outcome, justify action weights, control for ranking confounds, and specify your effect estimates and statistical approach.
  • You're launching unconnected content into a feed that previously only showed friends' posts. Define success metrics and design an A/B experiment that accounts for network interference, novelty effects, and potential cannibalization of friends' content. Include your randomization unit, ramp plan, power calculations, primary KPIs, guardrails, and a rollout or rollback framework.

“This is the kind of question where you can talk for 20 minutes and still feel like you barely scratched the surface.”

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

Jun 2026
Meta MLE onsite focused entirely on a single open-ended ML system design problem around weapon sales detection on a marketplace. Dense question, lots of ground to cover, and I definitely didn't get to all of it cleanly.
  • Design an ML system to detect high-frequency weapon sale listings on a marketplace platform. Walk through training data collection and labeling, whether funnel-stage models share training data, feature engineering, model architecture choices, evaluation metrics given the high-stakes nature of the domain, deployment, and feedback loops.

“This is a lot to fit into one session.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta product sense interview, just the one question about designing a restaurant finder inside Facebook. Pretty open-ended and I think I underestimated how much the Facebook-specific context was supposed to shape the answer.
  • How would you design a restaurant finder app within Facebook?

“I jumped straight into features and kind of forgot I was supposed to be designing this inside Facebook specifically, not just a generic Yelp clone.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview focused entirely on a single marketplace notifications case. The depth they expected across product thinking, experiment design, and decision frameworks in one question was a lot to handle in real time.
  • A marketplace team wants to notify buyers when new listings match their interests. How would you figure out whether this feature is actually worth building?
  • Walk through how you'd design an experiment for this notification feature, including randomization unit, eligibility, duration, and how you'd think about sample size.
  • If purchase data is delayed or too sparse to use as your primary metric, what proxy metrics would you rely on and how would you make a launch decision if clicks improve but opt-outs and spam reports are also rising?

“This was the whole interview basically.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineering role. The whole session was basically one big design problem about building an in-memory bank from scratch, and they went pretty deep on data structures and complexity guarantees.
  • Design an in-memory banking system supporting account creation, deposits, withdrawals, a top spenders query, scheduled payments with cancellation, and a process() function that executes due payments in order. Discuss data structures, time complexity targets, and how you'd test correctness and handle edge cases like duplicate scheduling or partial failures.

“This one sprawled in every direction.”

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

Jun 2026
Meta machine learning interview, asked to design a ranking model for Instagram feeds. Pretty open-ended and I wasn't totally sure how deep to go on the ML side versus the product side.
  • Design a ranking model for Instagram's feed. Walk through your approach from problem framing to model selection and evaluation.

“I started with objectives which felt right, but I spent way too long debating engagement signals versus satisfaction signals before actually getting to any architecture.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a meaty content moderation question that covered both offline model evaluation and online experimentation design. Pretty technical for a single question but that's Meta for you.
  • You have a machine learning model that flags harmful content for removal or down-ranking. Walk through how you'd evaluate it offline using a labeled validation set, then design an online experiment to test it in production, including hypotheses, variants, success metrics, and guardrails.

“This is basically two questions stitched together and I didn't pace myself well.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product design question about hashtags on Instagram. Pretty open-ended, which I wasn't fully prepared for.
  • Design a feature around hashtags for Instagram. How would you make hashtags more engaging for users?

“I went straight to listing features and the interviewer kind of just waited for me to finish before asking 'but who are you designing this for?' which, yeah, fair point.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta coding screen, pretty much one question the whole time. BFS on a grid, which I've done before but still managed to fumble parts of it under pressure.
  • Given a 2D grid with a start cell, a goal cell, and some walls, find the shortest path from start to goal. You can move in four directions and each step costs one unit. Return either the path itself or its length.

“I knew it was BFS the second I read it, which almost made it worse because I got overconfident and skipped thinking through edge cases.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineer role. One question, Instagram, but it sprawled into a bunch of follow-ups and I left feeling like I'd covered the right ground in the wrong order.
  • Design Instagram, covering core features like posting, news feeds, likes, and comments.

“I knew this type of question was coming and still managed to fumble the structure.”

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