← Back to Directory

Meta interview notes, page 138 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
Got a product sense question for a PM role at Meta, specifically about Horizon Worlds. Pretty niche product to get asked about, which made it trickier than a typical improvement question.
  • How would you improve Meta Horizon Worlds?

“The product itself is a bit of a minefield.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta, one question about measuring success for Facebook Fundraisers. Pretty focused session, no fluff.
  • How would you define and measure success metrics for Facebook Fundraisers?

“I went straight for the obvious stuff like total funds raised and number of donors, then kind of stumbled when they pushed back asking what "success" even means for the platform vs the fundraiser creator vs the recipient.”

View Post

Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round, one question on parentheses cleanup. Pretty standard string manipulation problem but the O(n) constraint meant you couldn't just brute force it.
  • Given a string containing lowercase letters and parentheses, remove the minimum number of parentheses to make the string valid. Must run in O(n) time.

“My first instinct was a stack to track unmatched opens, then a second pass to mark unmatched closes.”

View Post

Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta data engineer interview with a system design prompt centered on building an ETL pipeline for a Slack-like product in an educational setting. Single question, pretty open-ended, and the lack of constraints made it harder than expected.
  • Design an ETL pipeline for a Slack-like messaging platform used in a school environment.

“The educational context threw me a bit.”

View Post

Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Meta ML Engineer interview with a debugging-focused coding round. They handed me a broken maze solver and told me to find the bug. Pretty straightforward premise but the pressure of doing it live made me second-guess myself more than I should have.
  • You're given a BFS/DFS maze-solving implementation on a 2D grid that has a bug causing infinite loops or repeated cell processing. Find the bug and fix it so the search terminates correctly and returns a valid path.

“The fix itself isn't hard once you see it.”

View Post

Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design round, one big question about ad-click tracking at massive scale. The scope was brutal and I felt like I was playing whack-a-mole with follow-ups the whole time.
  • Design an ad-click tracking system that handles around 100 million click events per second, supports a near-real-time advertiser dashboard showing impressions, clicks, CTR, and conversions, and stores data for analytics and billing purposes.

“I started with the ingest layer which felt safe, load balancers into edge collectors into Kafka, and the interviewer just nodded and waited.”

View Post

Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding screen for a software engineer role, one question the whole time: implement a number validator from scratch. Seemed straightforward until I started thinking about all the edge cases.
  • Implement a function isValidNumber(s) that returns true only if the input string represents a valid number, following specific rules: optional leading sign, a mantissa with at most one decimal point and at least one digit, an optional exponent part starting with 'e' or 'E' followed by an optional sign and at least one digit, and no whitespace or special characters anywhere. Return false for inputs like 'e3', '+.', '1.2.3', '--6', '6e', and '6e-'.

“I started confident and immediately regretted it.”

View Post

Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta onsite system design round with two back-to-back problems: a large-scale leaderboard service and a real-time messenger. Each required full end-to-end coverage and the pace was relentless. Not a round where you can afford to ramble.
  • Design a large-scale leaderboard service that supports global rank, friend-circle rank, and paginated top-N queries with high write throughput.
  • How would you handle tie-breaking and stable pagination windows in the leaderboard?
  • How would you handle anti-cheat and score integrity in the leaderboard system?
  • Walk through how you'd support seasonal resets and multi-game leaderboards without disrupting the core service.
  • Design a real-time messenger supporting 1:1 and group chat, with correct message ordering and at-least-once delivery guarantees.
  • How would you design presence indicators, typing indicators, and read receipts at scale?
  • How do you handle fanout for very large group chats?
  • How would you approach end-to-end encryption in the messenger without the server being able to read message content?
  • How would you handle offline sync and multi-device consistency for the messenger?

“The friend-circle rank part is where I got tripped up.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview, one product design question about building a handyman-finding product. Pretty open-ended, which sounds freeing until you're actually in it.
  • You're a PM at Meta. Design a product that helps users find a handyman.

“I started by trying to anchor on why Meta specifically would build this, which felt smart in the moment but ate up a lot of time before I even got to user segments.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
SQL-heavy Data Scientist screen at Meta, six questions deep, all centered on a single schema with users, shops, visibility events, and calls. The problems escalated fast from basic aggregations to window functions and bias discussion, which I was not fully prepared for.
  • Given a visibility_events table, write a query for a single day that returns each shop's unique viewer count and average dwell time (visible rows only), filtered to shops with at least 2 unique viewers, sorted by viewer count descending then shop_id ascending.
  • Join the shops table to visibility events and compute, by category, the share of visible events that appeared in position 3 or above (top-of-feed). Round to 3 decimal places and sort descending.
  • Using a window function, compute for each user the time gap in seconds between consecutive visible events on a given day. The last visible event per user should return NULL.
  • Bucket users by account age in days into four ranges, define an 'active' user as having at least one visible event with dwell >= 5 seconds today, and compute DAU, active users, and active rate per bucket using only today's data. Also explain one bias introduced by restricting the denominator to users seen today.
  • Count distinct users who made or received at least one call on a given day. Write two versions: one using UNION and one using UNION ALL. Explain exactly when they produce different results on the sample data, and state the general rule for when UNION ALL is safe vs. when it double-counts.
  • Using a window function, compute a 3-event moving average of dwell_seconds per shop ordered by event time, using ROWS BETWEEN 2 PRECEDING AND CURRENT ROW.

“Pretty standard GROUP BY / HAVING stuff.”

View Post