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Meta interview notes, page 129 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

May 2026
Meta data scientist interview with a stats-heavy question on skewed distributions. Just one scenario but it packed in enough to keep me second-guessing my answer for days afterward.
  • Page share counts follow a heavy-tail distribution. How would you annotate key statistics like mean, median, P1, and P99, and then forecast future share volumes?

“I started with the easy part, explaining why mean is misleading here and median is more representative, but then kind of fumbled when they pushed on P1 and P99 specifically.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round, one question on course scheduling. Pretty standard graph problem if you've seen it before, but the pressure of doing it live is a different thing entirely.
  • Given a number of courses and a list of prerequisite pairs, determine whether it's possible to complete all courses without getting stuck in a circular dependency.

“Cycle detection in a directed graph, basically.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding screen, one problem the whole time. Pretty focused session, basically just this range query thing with a binary array.
  • You have a large, immutable binary array (only 0s and 1s). Given two indices i and j, how would you efficiently return the count of 1s between those indices, inclusive? The array could be queried many times.

“The key constraint is that the array is immutable and queries happen repeatedly, so preprocessing is the move.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta, one question about managing difficult employee situations. Pretty standard people-management territory but the dual framing (one you handled well, one you didn't) is a bit of a trap if you're not ready to be genuinely candid about a failure.
  • Tell me about the most difficult employee situation you handled well, and one you didn't handle so well.

“The second half is what gets you.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview that leaned heavily on AI usage questions. Left feeling uncertain, mostly because I kept second-guessing what they actually wanted to hear.
  • How do you use AI tools efficiently in your day-to-day work, and what pitfalls have you run into?

“I talked about prompting and tweaking AI-generated code at work, which felt like a reasonable answer at the time.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Interviewed at Meta, got asked a pretty classic stats question that sounds easy until you're actually on the spot trying to explain it clearly.
  • How would you explain statistical significance?

“I knew this cold, or thought I did.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product sense round at Meta, one question about a feature policy decision. Not much to go on from the session itself but the question was more nuanced than it looked.
  • Should Facebook remove the requirement for users to upload a profile picture during sign-up?

“I went straight to user friction and conversion rates, which felt right, but I missed the trust and safety angle for way too long.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview, product sense round focused on sports. Just one question but it opened up into a pretty sprawling conversation about features, users, and what Facebook even means in the sports context anymore.
  • You're a PM for Facebook Sports. What would you build?

“Spent the first minute just trying to figure out what Facebook Sports even is as a product surface, which probably wasn't a great look.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview focused on experiment design for a new video ad format. One meaty question that covered a lot of ground, felt more like a product case than a pure stats problem.
  • Design an experiment to evaluate the effectiveness of a new video ad format. Walk through your choice of randomization unit, primary and secondary metrics, how you'd think about sample size and statistical power, and what success looks like. Also, if the primary metric doesn't move significantly, what do you do next?

“This one sprawled in ways I didn't fully anticipate.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS technical screen, basically one long SQL problem about a group calls experiment. The schema was handed to you upfront which was nice, but the three sub-tasks together took up the whole session and there was a lot to keep track of.
  • Write a SQL query to compute, by country, the share of treatment-group users who started or joined at least one group call during the experiment week. Only include users exposed on or before a specified cutoff date.
  • Write a SQL query to estimate cannibalization of 1:1 calls using a difference-in-differences approach: compare per-user one-on-one call counts between treatment and control, across the experiment week and the pre-period.
  • Write a SQL query to identify the top 5 calls during the experiment week where both treatment and control participants were present in the same call, showing counts of each variant per call.

“The country join tripped me up at first because you pull country from the users table but the experiment table only has user_id.”

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