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Meta interview notes, page 173 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

Jun 2026
Meta PM interview, one question that sounds breezy until you realize they want you to think like a competitor and not just a fan.
  • What's your favorite product, and how would you go about building a direct competitor to it?

“I picked a product I genuinely love, which in hindsight made the second part harder because I kept defending it instead of attacking it.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
Meta DS interview with a meaty product case about ad ranking. The whole thing was one big open-ended question that kept branching into metrics, experimentation, and stakeholder tradeoffs. Felt less like a structured interview and more like a live product review with someone who already knew the answer.
  • Meta is thinking about boosting the ranking of ads that lead users to an in-app Shop experience compared to ads that send users to an external website. Walk through whether this is a good product and business idea, covering the hypothesis, stakeholders, benefits, risks, metrics, and how you'd test it.
  • How would you define success metrics and guardrail metrics for this ranking change, and what tradeoffs would you consider across user experience, advertiser outcomes, and platform revenue?
  • Design an A/B test to measure the impact of upranking Shop-destination ads. What would you randomize on, and how would you structure the experiment?
  • What sources of bias or confounding would make observational data misleading if you tried to evaluate this change without running a proper experiment?
  • Based on your analysis, how would you decide between a global launch, a segmented rollout, or not launching at all?

“This is basically six questions rolled into one, and I didn't realize that until I was already two minutes into talking about small advertisers.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round focused entirely on building an LLM-based agent system from scratch. It was a deep, sprawling question that touched everything from architecture to safety to cost, and I felt like I was constantly playing catch-up on the non-functional stuff.
  • Design an LLM-based agent system end to end, covering functional requirements like task decomposition, tool calling, memory, multi-step reasoning, and multi-agent coordination, plus non-functional concerns like latency, cost, reliability, and observability.
  • How would you handle evaluation and safety for an agent system like this?
  • How would you scale this system to support many concurrent agent runs?

“This is a monster of a question.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Meta for a PM role, focused entirely on a product you led and what you'd do differently. Pretty introspective stuff, less about metrics and more about self-awareness.
  • Which product you led are you most proud of, and what makes you feel that way?
  • Looking back at that product, what would you do differently?
  • What were the key lessons you took away from that product experience?

“This sounds easy until you're sitting there second-guessing every product you've ever touched.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one question on LCA in a binary tree. Pretty standard tree problem but the edge cases can trip you up if you're not careful.
  • Given the root of a binary tree and two nodes p and q, find and return their Lowest Common Ancestor.

“The second example is what gets people.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Data Scientist interview at Meta centered almost entirely on a bad-account detection scenario. The questions ranged from basic probability to model evaluation to platform-level thinking, so you need to be comfortable jumping between math and product reasoning in the same conversation.
  • Given that 1% of accounts are bad and bad accounts send friend requests at 10x the rate of good accounts, what's the probability that a single received friend request came from a bad account?
  • Out of five friend requests received, what's the probability that at least one comes from a bad account?
  • Your classifier has a 95% true positive rate and 95% true negative rate. If it flags an account as bad, what's the actual probability that account is truly malicious?
  • What types of data and features would you use to build a classifier that distinguishes bad accounts from good ones?
  • How would you estimate whether the bad account problem is large enough to actually do something about it?
  • How would you define what makes an account 'bad' in the first place?
  • How does a significant presence of malicious users affect the platform's health and reputation?
  • What are the downstream effects on real users when bad accounts send friend requests at high volume?
  • How would you think about adjusting the classification threshold, and what are the tradeoffs between false positives and false negatives here?
  • How would you scale this detection system fairly across billions of accounts without creating disproportionate harm for certain user groups?

“This is a Bayes problem dressed up in product clothing.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview for Facebook Watch, one scenario question about a redesign that pulled watch time up but tanked engagement metrics. Short and focused, but the tradeoffs made it genuinely tricky to reason through cleanly.
  • You're a PM on Facebook Watch and a redesign improved watch time but caused likes and comments to drop. Do you ship it?

“My first instinct was to say yes because watch time feels like the more meaningful signal for a video product.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Meta with a question that sounds operational but is actually a strategy and stakeholder problem in disguise. One question, fairly open-ended, left me wishing I'd thought harder about the people side before jumping into process.
  • How would you approach decommissioning a service or system?

“I went straight to the technical checklist stuff: deprecation timeline, migration path, comms plan.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Meta MLE coding round, two LeetCode problems back to back. Pretty standard stuff if you've done your prep.
  • LeetCode 393: UTF-8 Validation. Given a list of integers representing data, determine if it encodes a valid UTF-8 sequence.
  • LeetCode 419: Battleships in a Board. Count the number of battleships on a 2D board without using extra memory.

“Bit manipulation problem.”

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Meta·Data Scientist·Onsite - System Design / Architecture

Jun 2026
Meta DS interview for a ranking/recommendation role. One massive system design question that covers basically everything you'd ever need to know about ML-powered ranking. It was a lot to hold in your head at once.
  • You own the restaurant recommendation surface for a city app. Design a ranking system that balances distance, predicted satisfaction, exploration of new restaurants, and diversity across cuisines. Cover: feature engineering from sparse signals, a learning-to-rank approach that avoids popularity bias and handles cold-start, a feedback-loop mitigation plan, online serving constraints with a fallback, offline/online evaluation with position-bias correction, and how you'd detect and fix geographic and price-point unfairness without hurting user utility.

“This question is basically five questions stapled together.”

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