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Meta interview notes, page 96 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

May 2026
Meta PM interview, product sense round, single question about improving Zoom. Pretty lean on details but the question itself is a classic product redesign prompt.
  • How would you improve Zoom?

“Competitor product questions are tricky because you have to pick a real user pain point without sounding like you're just listing features.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta Data Scientist interview focused entirely on a model replacement scenario for the Ads team. Four connected sub-questions that built on each other, which I wasn't expecting. Pretty intense for what felt like a single case.
  • Before fully replacing an existing recommendation model with a new one, what factors need to be evaluated?
  • If CTR drops but revenue or other business metrics improve after switching models, how do you decide which one to ship?
  • Walk through the A/B testing procedure you would use to evaluate the new model against the old one.
  • How would you visualize and present the final model comparison results to a CFO?

“I jumped straight to offline metrics and kind of forgot about rollback plans until the interviewer nudged me.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS technical screen, two SQL questions back to back focused on ad event data. Nothing too wild conceptually but the deduplication and division-by-zero edge cases were clearly the point of the whole thing.
  • Write a SQL query to compute the overall click-through rate (clicks divided by impressions) for the last week, deduplicating identical event logs and handling the case where impressions could be zero.
  • Extend your CTR query to break down click-through rate by campaign type, joining to a campaigns table, and still handling deduplication and zero-impression edge cases.

“The dedup part is what trips people up.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data science interview with a heavy stats focus. Two questions, both probabilistic modeling, and they expected you to actually derive things rather than just name-drop distributions.
  • Assume user session durations follow a memoryless process. Derive the resulting distribution, explain the memoryless property, and describe two empirical ways to check whether real data actually fits this assumption using survival plots or hazard estimates.
  • A user views M posts and independently comments on each with small probability p. Justify using a Poisson model for the total comment count, give its parameter in terms of p and M, and describe what diagnostic signals would point to overdispersion or zero inflation and what model you'd use instead.

“The derivation part was fine, exponential distribution, constant hazard, I've seen it before.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product sense question for a PM role at Meta. Just the one question but it's a meaty one, climate change as a product space is genuinely tricky to navigate for a social/ad platform.
  • As a PM at Meta, what product would you build to help address climate change?

“This kind of question always trips me up because the obvious answers feel too easy and the creative ones feel disconnected from what Meta actually does.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a SQL-heavy ops analytics problem. Two questions back to back, both revolving around the same two tables, which felt a bit repetitive but also meant you could build momentum if you got the first one right.
  • Given a table of content views with dates and view counts, write SQL to return the count of distinct posts that accumulated more than 10 views within the past 7 days (inclusive).
  • Using the same content views data joined with a violations table, write SQL to calculate the view-prevalence of Spam or Scam posts over the last 30 days, defined as total views for those violation types divided by total views across all posts.

“My first instinct was to just filter on a hardcoded date, which would've been wrong.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a product analytics case focused on their video ads business. One meaty question that spiraled into a bunch of sub-parts about spend drops, root cause isolation, and experiment design. Felt like a real on-the-job scenario more than a textbook problem.
  • An advertiser reports that their Brand Ads spend has dropped significantly over the past two weeks. How would you figure out whether the drop is real, whether it's isolated to this advertiser or a broader issue, identify the root cause, and recommend follow-up analyses or experiments?

“This one kept expanding.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE phone screen with two questions that don't obviously belong together. One classic string problem, one custom arithmetic implementation. Short session, left me second-guessing how I did.
  • Given a string, determine whether any permutation of it can form a palindrome.
  • Given two non-negative numbers represented as decimal strings (which may include a decimal point), implement a function that adds them and returns the result as a string.

“Knew the trick immediately: count character frequencies, at most one can be odd.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Stats fundamentals check for a Data Scientist role at Meta. Three questions, all centered on CLT and confidence intervals, with a whiteboard component at the end. Pretty textbook stuff but the whiteboard part tripped me up more than I expected.
  • Can you state the Central Limit Theorem and explain why it's relevant to A/B testing?
  • Given a sample mean of 3, a known population standard deviation of 1, and n = 400, build a 95% confidence interval for the mean.
  • Sketch the sampling distribution on the whiteboard and mark where the confidence interval bounds fall.

“I knew the definition cold but fumbled connecting it to A/B testing in a crisp way.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta product sense interview, one question about designing a feature for collectible enthusiasts on Facebook. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product for collectible lovers on Facebook.

“I went straight to marketplace integrations and kind of ignored the community angle for way too long.”

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