Meta Interview Questions — Page 105

Meta·Data Scientist·Technical Phone Screen

May 2026

SQL round for a DS role at Meta, one question but it had enough moving parts to keep me busy for a while. The ad metrics angle felt pretty domain-specific for a marketplace context.

Write a SQL query that produces daily ad performance broken down by country for the last 7 complete UTC days. The output should include impressions, clicks, revenue, CTR, and RPM per (date, country) pair, joining impression and click data correctly and handling impressions with no clicks.

“There's a lot going on here.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026

Meta MLE coding round focused on a classic expression evaluation problem, but with a twist: they wanted two distinct implementations and a complexity discussion. Not the hardest problem on paper, but the O(1) space variant tripped me up more than I expected.

Implement a calculator that evaluates an arithmetic expression with non-negative integers, '+' and '*' operators, and optional whitespace. No parentheses. Standard operator precedence applies. Provide both a split-based solution and a single-pass O(1)-space solution, and compare their tradeoffs.

“The split approach came naturally: split on '+' to get additive terms, then split each term on '*' and multiply across.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026

Meta PM interview with a product design question around celebrity engagement. Single question, pretty open-ended, and I spent the first few minutes just trying to figure out what they even meant by 'celebrity engagement' before committing to a direction.

Design a product for celebrity engagement.

“I went in thinking this was about fan-to-celebrity interaction but halfway through realized the interviewer probably cared just as much about the celebrity side of the equation.”

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Meta·Software Engineer·Technical Phone Screen

May 2026

Meta SWE coding round, one problem the whole session. It was a variant of the Freedom Trail problem and I spent way too long second-guessing my DP approach.

Given a string of N characters arranged in a circle, find the minimum number of steps (clockwise or counter-clockwise) needed to spell out a target string by selecting characters from the ring in order. After each character is selected, you may start from any position on the ring.

“I recognized it as a DP problem pretty quickly but fumbled the state definition for a while.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

Meta data scientist interview with a heavy stats/experimentation focus. The whole thing was basically one long scenario about a noisy A/B test, broken into four parts that got progressively more uncomfortable to answer under pressure.

You ran a two-sample t-test with 80% power and observed a positive effect, but the result wasn't statistically significant. Why can that happen even with a large sample?
Should you calculate post-hoc power after seeing the results? If not, what should you report instead and why?
Given a loss function where false positives are twice as costly as false negatives, how do you make a decision here and what are your next steps: extend the experiment, reduce variance, or stop?
How would you redesign the experiment for the next iteration, specifically around the MDE, variance reduction techniques, and duration?

“I knew the textbook answer here but fumbled the delivery a bit.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

Meta data scientist interview with a stats-heavy question on distributions and the central limit theorem. Pretty theoretical, felt more like a grad school oral exam than a product interview.

Sketch a right-skewed distribution of users' daily comment counts and mark where the mean, median, and 95th percentile fall. Then suppose you repeatedly sample random groups of users and compute each group's average comment count. What does that new distribution look like, and what happens to the mean, median, and 95th percentile?

“The sketch part was fine, mean to the right of median, long right tail, 95th percentile way out there.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round at Meta for a Data Engineer role. Four questions, all with the expectation of deep follow-ups on metrics and decisions. Pretty standard for Meta but the depth they go into on each one is real.

Tell me about a conflict you had with a colleague or stakeholder. How did you figure out what was actually causing it and how did you resolve it?
Describe a project where the deadline was fixed and couldn't move. How did you plan, cut scope, and communicate the trade-offs? What ended up happening?
What's the most significant project you've owned from start to finish? What was your specific contribution and what measurable impact did it have?
Give an example where you used data or an experiment to change someone's mind. What did the analysis show, how did you handle pushback, and what decision got made?

“The tricky part isn't telling the story, it's the diagnosis piece.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026

Meta data engineer interview, system design round focused entirely on data modeling for a social news feed. Pretty technical and went deeper than I expected, especially with the follow-up on sharing.

Design a data model for a social app's news feed that supports multiple content types like text, images, and short videos. Walk through the core entities, relationships, and how you'd handle primary and foreign keys.
In an analytics warehouse context, which of these tables would be dimensions and which would be facts? What are the core fields you'd include?
How would you extend the model to support sharing, where a user can share content to another user, to a group, or to an external channel?

“I started with a users table and a content table, then tried to handle the multiple content types with a type column plus separate attribute tables per type.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

A product DS case for Meta's feed team. The whole thing was one big multi-part scenario about introducing unconnected content into the Info Stream, and it went pretty deep into experiment design, metric tradeoffs, and what to do when your results are messy.

Using impression, view, and reaction data, how would you validate that content from a user's friends drives stronger engagement than content from unconnected authors? Walk through the metrics you'd define, your hypotheses, and how you'd control for confounders like feed position and content type.
Design an experiment to measure whether launching unconnected content into the feed is successful. Cover randomization unit, treatment arms, experiment duration, statistical power, primary success metrics, and guardrail metrics.
How would you interpret the experiment results, including potential cannibalization of friend engagement and segmentation effects? What would your launch decision and rollout plan look like?
If unconnected viewers show lower per-impression engagement, what alternative value could they still provide, and how would you quantify it?

“This is where I spent most of my time and also where I stumbled a bit.”

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Meta·Product Manager·Hiring Manager Screen

May 2026

Interviewed at Meta, got asked one of those reflective project questions that sounds easy until you're actually sitting there trying to pick the right story.

Looking back on your most recent project, what would you have done differently?

“I picked a project that was too recent and still felt raw.”

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