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Meta interview notes, page 191 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·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Meta EM interview with a product design question about building an auction feature on Instagram. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design an auction feature for Instagram?

“I spent too long on the user experience side and didn't get deep enough into the monetization mechanics or how bidding would actually work under the hood.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
A deep-dive metrics design interview for a DS role at Meta, all centered on content moderation measurement for a UGC platform. The questions were layered and kept building on each other, which felt more like a technical case study than a standard interview.
  • Define a metric framework for measuring user exposure to violating content on a UGC platform. Include precise formulas for at least three daily metrics and their 7-day rolling versions.
  • How do you define what counts as a 'violating view'? Walk through your inclusion and exclusion rules under both an ex-ante regime and an ex-post regime, and explain how you'd handle late-arriving labels, appeals, deleted content, repeat views, and bot traffic.
  • Is view_prevalence a good north-star metric? Compare it to incident_rate (violating items over items created) and user_prevalence (users exposed over DAU). What are the tradeoffs?
  • Propose a weekly alerting system for these metrics. How would you set thresholds using uncertainty estimates, and what guardrail metrics would you track alongside them?
  • Design an A/B test to reduce view_prevalence. What is your primary metric, how do you segment the experiment, and how do you correct for label latency and selection bias when violations are discovered after the exposure window?

“I started with view_prevalence (violating views over total views), then sessions with at least one violating view over total sessions, then violating views per DAU.”

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

Jun 2026
Interviewed for an ML engineer role at Meta and got hit with a system design question about building an auto-complete feature. Pretty open-ended, which I wasn't totally prepared for.
  • How would you design an auto-complete feature?

“Spent the first few minutes just trying to scope it properly because 'auto-complete' could mean a search bar, a code editor, a messaging app, all different beasts.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Product design question for Meta, basically one big open-ended prompt about building a travel solution for Facebook employees worldwide. Not much else to go on from the session itself.
  • Facebook has offices all over the world and employees travel constantly. Design a product solution to support employee travel at Facebook.

“Big open canvas, which sounds great until you're staring at it.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineer role. The problem was a real-time chat system, basically a WhatsApp clone, and it covered more ground than I expected in the time given.
  • Design a chat system similar to WhatsApp or Messenger, supporting both 1:1 and group conversations.
  • How would you handle message delivery states like sent, delivered, and read across clients?
  • How do you ensure messages within a conversation are ordered correctly, especially when clients send messages at nearly the same time?
  • How would you sync message history across multiple devices for the same user?

“I started with the connection layer and spent probably too long on WebSocket vs polling before getting nudged toward the actual storage design.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Meta, one question focused on Reels strategy. Pretty open-ended but with a lot of implicit pressure to show you understand Meta's business model and where Reels fits in the competitive landscape.
  • You're a PM at Meta. Make the case for why Meta should keep investing in Reels, and define the goals and metrics you'd use to measure success.

“I started with the competitive angle (TikTok, obviously) which felt right but I spent too long there and barely had time to get into the metrics side before they nudged me along.”

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Meta·Software Engineer·Recruiter / HR Screen

Jun 2026
Behavioral screen at Meta, just the one question about a frustrating tech experience. Pretty standard but worth logging.
  • Tell me about a recent frustrating experience you had with technology.

“Picked something real but maybe too mundane.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Went through a PM behavioral round at Meta and got hit with a trust-building question that felt more Amazon than Meta to me.
  • How do you build trust with the members of your team?

“I knew this type of question was coming but still fumbled the opening a bit.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Two back-to-back system design questions at Meta for a software engineer role. Both were big, open-ended problems and the session felt more like a whiteboard marathon than a conversation. Walked out unsure if I covered enough depth on either one.
  • Design a large-scale online event ticketing platform similar to Ticketmaster, covering seat availability, purchasing, and handling traffic spikes during popular on-sales.
  • Design an online coding practice platform where users can write, run, and submit code in multiple languages, with secure sandboxed execution and global scale.

“I spent way too long on the browse/search flow and then scrambled when they pushed on concurrency for seat holds.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta data science interview with a product analytics question about cross-platform feature decisions. Single question, felt more like a product sense round than a pure data science one.
  • A feature launched successfully on Facebook. How would you advise the Instagram team on whether to launch the same feature on their platform?

“My first instinct was to just say 'look at the Facebook metrics and replicate the rollout' which is obviously too shallow.”

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