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Meta interview notes, page 56 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

Apr 2026
Meta product strategy interview, one question about Spotify. Not much else to go on but it was a meaty enough prompt to fill the whole session.
  • Suggest a strategy for Spotify.

“Wide open question and I kind of froze for the first minute trying to figure out where to anchor.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview with a classic metrics problem. Nothing too wild but it made me realize how easy it is to spiral on these if you don't slow down first.
  • Uber's booking rates have dropped by 10%. How would you investigate and address this?

“I jumped straight into brainstorming fixes before even clarifying what 'booking rate' meant in context.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview centered on a single meaty product analytics case about launching a group video call feature. The question had a lot of moving parts and I felt like I was juggling too many threads at once.
  • You have access to messaging data, friend graphs, and survey results. How would you measure latent demand for a group video calling feature, define success metrics for its launch, design an experiment to evaluate it, and use data to decide on an initial cap for the number of participants per call?

“This is four questions stapled together and I did not handle the transition between parts gracefully.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview with a single product strategy question about Meta AI in Instagram DMs. Pretty open-ended, covers investment rationale, goal-setting, and metrics all in one shot.
  • Is integrating Meta AI into Instagram DMs a worthwhile investment for Meta? Why or why not? What goals would you set, and what metrics would you use to track success and monitor product health?

“This one is basically three questions stacked on top of each other and I didn't fully appreciate that until I was already halfway through the investment rationale.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview focused almost entirely on a video recommendation push system, mixing probability theory with product thinking and ML model selection. Pretty math-heavy for what I expected to be more of a product-flavored round.
  • Given an inventory of N videos, what is the probability that a specific ordered sequence of k videos gets pushed to a user? And what changes if the order doesn't matter?
  • Given the probability of an event, how do you compute its complement, and how does that apply to this video selection scenario?
  • Should the system push the same video to two friends, or different videos? Walk through the tradeoffs and what metrics you'd expect to move.
  • What statistical or ML model would you use for this video recommendation problem, and what's your reasoning?

“The ordered vs unordered distinction is where I slipped up a little.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, pretty much what you'd expect. One interval problem and then you're done.
  • Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping intervals.

“Sort first, that's the key move.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta Research Scientist coding screen, basically a sparse vector problem with a twist. The core question was straightforward but they pushed into follow-up territory pretty fast and that's where things got interesting.
  • Given two sparse vectors of equal length, implement an efficient representation (no full dense array) and a dot product method. Then be ready to extend your solution to a follow-up variant the interviewer introduces.

“I went with a sorted list of (index, value) pairs and a two-pointer merge to compute the dot product.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta data scientist interview with a probability/Bayesian question that felt more like a stats exam than a job interview. One question, pretty focused, no fluff.
  • A platform's fake-account detector has a 2% base rate of fake accounts, flags 90% of real fakes, and incorrectly flags 5% of legitimate accounts. If an account gets flagged, what's the actual probability it's fake? Walk through your math.

“This is pure Bayes theorem and I knew that immediately, but I still fumbled the setup for a second because the base rate being so low felt counterintuitive.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Phone screen at Meta for a senior SWE role. Just one coding round, started with a classic anagram grouping problem as a warm-up before presumably moving to something harder.
  • Given a list of strings, group all strings that are anagrams of each other and return the groups.

“Warm-up question, but the clock pressure is real on phone screens.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview focused entirely on SQL, specifically around group video call data. Multiple questions on the same dataset, which I wasn't expecting. The schema was given during the interview so you're working with whatever they hand you.
  • Given tables for users, video call sessions, session participants, and event logs, write SQL to compute key metrics around group video call usage. Define what counts as a 'group' call and explain your logic.
  • A user may have multiple join and leave events in the same session. How do you handle deduplication when calculating whether a user participated in a call?
  • Write SQL to aggregate group video call participation at a daily and weekly granularity.

“The definition question tripped me up more than the actual SQL.”

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