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Meta interview notes, page 126 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 - Behavioral / Leadership

May 2026
Behavioral round at Meta, one question about feedback and self-improvement. Not a lot to go on but it's the kind of question that sounds easy until you're actually in it.
  • How do you receive and act on continuous feedback, particularly about your own weaknesses? What do you do to make sure you're actually improving and not making the same mistakes again?

“I went with a real example from a past job where my manager kept flagging the same communication issue in every review.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta product sense round, got handed a digital well-being prompt and had to run with it. Pretty open-ended, which sounds fun until you're actually in the room.
  • Design a product for digital well-being.

“I went straight to screen time controls and basically reinvented something Meta already ships.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta Data Scientist SQL round, focused entirely on ad analytics. Two related questions, both involving the same tables, just different levels of aggregation. Nothing too wild but the CAST thing is easy to forget under pressure.
  • Write SQL to compute the overall click-through rate (clicks divided by impressions) across all ads for the last calendar week.
  • Now break that CTR down by campaign type. Same time period, but join to the campaigns table and group by campaign_type.

“The mechanics are straightforward: filter by event_date, count impressions and clicks separately using conditional aggregation, then divide.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview focused entirely on trust and safety measurement. One big open-ended case about quantifying harmful content, broken into four parts. The depth they expected was real, this wasn't a vague 'how would you think about it' conversation.
  • Which metric would you choose as the north-star for measuring harmful content severity, and how would you decide between View Prevalence, Content Prevalence, and Reach Prevalence? Would you apply severity weighting, and if so, how?
  • What supporting metrics would you track alongside your primary harmful content metric? Things like user reach, tail exposure intensity, or time-weighted exposure.
  • Walk through the pros and cons of relying solely on View Prevalence as your harmful content metric. What does it capture well and what does it miss?
  • How would you make sure your harmful content measurement is unbiased and timely? Cover things like sampling strategy, human labeling, classifier calibration, confidence intervals, and segmentation.

“I went with view-weighted prevalence as the primary because it maps more directly to actual user exposure, not just how much bad content exists in the corpus.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta, one question about push notification metrics. Not much else to go on but it's the kind of question that sounds easy and then you realize mid-answer you've been too shallow.
  • What KPIs would you use to measure the success of push notifications?

“I went straight to open rate and click-through and felt pretty good about it, then realized I'd skipped the whole retention and opt-out side of things.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta, one question about Messenger metrics. Pretty focused session, no fluff.
  • How would you measure improvements made to Facebook Messenger?

“I went straight for engagement metrics and the interviewer just kind of waited, which made me nervous.”

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Meta·Machine Learning Engineer·Onsite - Coding / Algorithms

May 2026
Meta ML engineer coding round, one question the whole time. Pretty focused on string manipulation with a twist that made it trickier than it looked.
  • Write a function that removes the minimum number of characters from a string to make all parentheses balanced and properly nested.

“My first instinct was to just count opens and closes, which obviously doesn't handle nesting correctly.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a single product design question about tourist communication. Pretty open-ended, which sounds fun until you're actually sitting there with no anchor.
  • Design a new way for tourists to communicate.

“I went straight to language barriers and translation apps, which in hindsight was the most obvious framing possible.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding round for a Software Engineer role. One problem, but it had enough layers to keep me busy for the whole session.
  • Given an integer array, return a new array where each element is the product of all other elements in the original array. You must do this in O(n) time, without division, and using only constant extra space beyond the output array. Also discuss how you'd handle zeros, negative numbers, and overflow, and write unit tests.

“I knew the prefix/suffix product trick going in, so the core algorithm wasn't the hard part.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta SWE coding round, one question the whole time: alien dictionary. Felt manageable going in but the corner cases have a way of humbling you mid-implementation.
  • Given a list of words sorted in an unknown alphabet's lexicographic order, return a valid ordering of the letters in that alphabet. Return an empty string if no valid ordering exists.

“I knew the general shape of the solution: build a directed graph from adjacent word pairs, run topological sort.”

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