Meta Interview Questions — Page 72

Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round at Meta for a software engineering role. Just one question but they sat on it for a while, really digging into how I handled a specific disagreement with leadership.

Tell me about a time you strongly disagreed with a leadership decision. Did you ultimately hold your ground or compromise, and how did it play out?

“This one has more layers than it looks.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

Meta DS interview focused entirely on experimental design for a social feature with real network effects. The questions went deeper than I expected, especially around what happens when you can't cleanly cluster users.

Walk through an end-to-end experiment design for testing a group video calling feature: hypothesis, randomization unit, sample size, and duration.
Given strong network effects in a social product, how would you choose the randomization unit for this experiment?
If clustering isn't feasible, what alternative experiment designs can reduce interference between treatment and control groups?
Which statistical tests would you use for continuous metrics versus proportion-based metrics in this experiment?
How would you present the experiment results differently to a non-technical PM versus a data science peer?

“This felt manageable at first but I tripped on sample size.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026

Did a product sense round at Meta for a PM role, got a market sizing question which I half-expected but still fumbled my way through.

Estimate the total market size of the bicycle market in the United States.

“I started with a population-down approach, segmenting by age and likelihood of owning a bike, then tried to layer in average price points for different bike types.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026

Meta PM interview focused entirely on payments product sense, specifically around Meta Pay. Three questions, all connected, and the third one is where things got interesting for me.

What would you define as the North Star metric for Meta Pay, and what supporting metrics would you use alongside it?
If you could only ship one feature next year for Meta Pay, a peer split-bill capability or a donations flow, which do you choose and why?
The North Star metric is flat but cost per transaction has dropped significantly. How do you investigate this?

“I fumbled the opening here.”

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Meta·Software Engineer·Technical Phone Screen

May 2026

Meta SWE coding round, one question: implement a doubly linked list. Pretty straightforward on the surface but these things have a way of tripping you up in the details.

Implement a doubly linked list from scratch.

“Thought I had this cold going in.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026

Two coding problems at Meta for an MLE role. Nothing ML-specific, just straight algorithms. The second problem was nastier than it looked.

Given an array of package weights and a number of days D, find the minimum ship capacity needed to deliver all packages in order within D days.
Given a digit string and a target integer, return all expressions formed by inserting '+', '-', or nothing between digits that evaluate to the target.

“Binary search on the answer.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

Meta DS interview focused on the Instagram Shopping Tab launch. The whole session was basically one big product analytics case study, walked through metrics, sizing, and dashboard design. Pretty structured but there was a lot of ground to cover.

What primary and secondary metrics would you define to evaluate whether the Instagram Shopping Tab is performing well after launch?
Before the Shopping Tab launched, how would you estimate the expected size of its impact?
What dashboards or visualizations would you build to monitor the Shopping Tab metrics on an ongoing basis?

“I went straight to GMV and click-through rate as primary, then fumbled a bit on secondary.”

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Meta·Data Scientist·Technical Phone Screen

May 2026

Meta DS interview with a heavy quant focus, basically one long fraud modeling case broken into three parts. The kind of question where you either live in this world daily or you're improvising the whole time.

You have a fraud scoring model outputting calibrated probabilities per account, with auto-block and manual review thresholds, a fixed daily review budget, and known costs for false positives, false negatives, and reviews. How do you write out the expected daily cost as a function of the two thresholds, and how would you estimate it from historical data using calibration techniques and empirical score distributions?
Given the review budget constraint, how do you jointly optimize the two thresholds? Walk through how you'd pick an operating point on the precision-recall curve and then validate the choice with an online test.
Describe how you would monitor for model and score drift over time, and outline a weekly process for re-tuning the thresholds including backtesting and guardrails to prevent bad updates from going live.

“This part took me longer than it should have to set up cleanly.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026

Meta SWE coding round built around a progressive maze solver problem, starting from bug fixes and working up to BFS with keys and doors. Four parts in one session, each layering on the last. It's the kind of problem that feels manageable until part 3 and 4 show up.

You're given a buggy maze-solver codebase with failing unit tests. Fix the bugs without changing the intended behavior so all tests pass.
Implement BFS to find the shortest path from start to exit in the maze. Return -1 or None if no path exists.
Extend the movement logic to enforce directional constraints on which moves are valid. Update the neighbor function accordingly and still return the shortest feasible path.
Extend the maze to support keys (a-f) and locked doors (A-F). You collect a key by stepping on it and can only pass through a door if you hold the matching key. Modify BFS state to track collected keys and return the shortest path from start to exit.

“Started here and it was actually a decent warmup.”

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Meta·Software Engineer·Technical Phone Screen

May 2026

Meta SWE interview covering a solid mix of graph traversal, sliding window, linked list manipulation, and some data structure design. Nothing too exotic but the range was wide enough that you had to be comfortable switching gears fast.

Find the maximum area of an island in a binary grid.
Design a data structure that supports insert, delete, and get random element, all in average O(1) time.
Remove the Nth node from the end of a linked list.
Given a binary array and an integer k, find the maximum number of consecutive 1s you can get if you can flip at most k zeros.
Compute the weighted sum of a nested list, where each integer is weighted by its depth level.
Given a string with parentheses, remove the minimum number of parentheses to make it valid.

“Classic DFS/BFS flood-fill.”

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