Meta Interview Questions — Page 236

Meta·Software Engineer·Technical Phone Screen

Jul 2026

Meta SWE coding round, one problem, bit manipulation territory. Not the hardest thing in the world but the negative number case will absolutely trip you up if you haven't thought about two's complement recently.

Given a 32-bit signed integer, return its hexadecimal string representation using lowercase letters, no leading zeros, and two's complement encoding for negative values.

“The zero case and positive case I got through fine.”

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Meta·Data Scientist·Onsite - Cross-functional / Panel

Jul 2026

Interviewed for a Data Scientist role at Meta and got hit with a meaty stakeholder scenario about bad causal inference from sales leadership. One question but it had five sub-parts and basically ate the whole session.

Sales leadership observed a correlation between call volume and win rate and wants to double required calls starting next week, claiming it will increase wins. The analysis is confounded by deal stage and rep mix. How do you push back diplomatically, propose a low-cost pilot, set timeline and data quality expectations, align stakeholders, and handle pressure to publish early directional results before the pilot has enough data?

“This one was brutal in the best way.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

Meta DS technical screen, all SQL, no fluff. Three questions off one schema and they just let you cook. The queries got progressively nastier and I definitely fumbled the percentile one before recovering.

Given a schema with users, posts, and comments, write a query to return the top 3 posts by distinct commenter count within a 7-day window, excluding deleted comments. Break ties by newer post date, then lower post_id. Include a count of distinct commenter countries.
For each post and day within the same 7-day window, compute the P50 and P90 of comment text length over non-deleted comments using percentile_cont. Output one row per post per day, only for days that have at least one qualifying comment.
Identify users who commented on their own posts within the 7-day window. Return user_id, the count of such self-comments, and the timestamp of the most recent one. Order by self-comment count descending, then user_id ascending.

“The tie-breaking tripped me up a bit.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Meta system design round for a software engineer role. The problem was an in-memory database with TTL support, scans, and backup/restore. Deceptively tricky once you get into the edge cases.

Design an in-memory key-field-value store where each entry can optionally expire after a given TTL. Implement set, set_with_ttl, get, delete, scan, scan_with_prefix, backup, and restore operations.

“The basic set/get/delete stuff came together fast, but I underestimated how much the TTL visibility rule would ripple through everything.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026

Meta data engineer screen with one algorithmic problem that felt more like a logic puzzle than a coding challenge. Pretty focused session, just the one question but they pushed hard on complexity at the end.

Given a chronological list of event logs in the form (timestamp, book_id, is_checkout), write a validator that checks whether each book's checkout/return sequence is valid. A valid sequence must start with a checkout, strictly alternate between checkout and return, and never have a return appear before any checkout. Return True if all sequences are valid, False otherwise. Also state the time and space complexity.

“I grouped events by book_id and iterated through each book's sorted events tracking the last seen action.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

Meta data scientist interview with a heavy stats focus. Four questions, all technical, no behavioral fluff. The kind of session where you either know your Bayesian priors or you don't.

Given an A/B test with 5 interim looks already done, compute the two-sided p-value and 95% confidence interval for the difference in conversion rates between control and treatment, then explain how O'Brien-Fleming alpha spending would affect whether you'd call the result significant.
You have five metrics with sorted p-values of 0.001, 0.012, 0.019, 0.070, and 0.300. Apply Benjamini-Hochberg at a 5% FDR and identify which are discoveries. Then compare to what Bonferroni would give you.
Your bot detection model has a 2% false positive rate and 10% false negative rate. Bot prevalence is 5%. If a user gets flagged, what's the actual probability they're a bot? Walk through the Bayes calculation.
Average Order Value is right-skewed with a heavy tail. Compare IQR-based outlier removal, z-scores, and MAD or Huber M-estimators for handling this. Then explain a log-transform approach and how you'd report the effect back in original dollar terms using a smearing estimator.

“This is the question I spent the most time on.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

Meta DS interview with a product health angle, basically one meaty SQL question dressed up as a real business scenario. The Oculus usage framing was a nice touch, made it feel less like a leetcode grind and more like actual work.

You're given a table of Oculus headset session data. First, define what 'unhealthy' usage looks like as a SQL-expressible rule (e.g. more than 120 continuous minutes per day for at least 3 consecutive days in the past month). Then write SQL to return the count of unhealthy users, total active users, and the percentage of unhealthy users over the last 30 days.

“The open-ended part tripped me up more than the SQL itself.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026

Meta SWE coding round with two tree problems. Nothing too exotic but the diameter modification tripped me up more than I expected.

Given a binary tree, return its vertical order traversal (nodes grouped by column, top to bottom within each column).
Find the diameter of a binary tree, but return the count of nodes along the longest path rather than the number of edges.

“Felt okay about this one.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026

Behavioral round at Meta for a software engineer role, focused on conflict and collaboration. Just the one question but they went deep on it.

Tell me about a time you disagreed with a colleague's approach at work but ultimately decided to go with their idea instead of yours. Walk through the situation, what you originally proposed, why you deferred, what you did to support the decision, and what came out of it.

“This is the kind of question where you think you have a good story and then halfway through you realize it sounds like you just caved.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026

SQL-heavy technical screen for a DS role on an ads platform. Two questions, both multi-part, both requiring you to actually think about the data model rather than just write a join and call it a day. Left feeling okay about Q1 but Q2 had a wrinkle that tripped me up a bit.

Given an ads platform schema with advertisers, ads, and daily spend tables, identify advertisers whose total 2023 spend exceeded $1,000. Then, for that cohort, calculate what percentage of total 2024 platform spend came from them. Return the eligible advertiser count, cohort 2024 spend, total 2024 platform spend, and the cohort share.
Write a query returning monthly 2024 spend broken down by ad creation source, excluding certain advertiser types. Also include, within each month, the spend from advertisers where AI-assisted ad spend went up year-over-year while their manual ad spend went down, to surface potential cannibalization between creation sources.

“The cohort definition part is pretty clean, just a subquery or CTE filtering on 2023 spend.”

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