Meta interview notes, page 38 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·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round for an MLE role at Meta. Three pretty standard questions, nothing that should've tripped me up but one of them did.

Tell me about a time you had a conflict with a teammate and how you resolved it.
Describe a significant professional failure and what you took away from it.
Where do you see your career going over the next few years?

“I had a decent story ready but I rambled into too much backstory before getting to what I actually did.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta SWE interview with a robot navigation problem that looked like a DFS/backtracking question but had enough moving parts to keep me second-guessing myself the whole time.

Design an algorithm to navigate a robot to a target position in a room of unknown size, using only move(), turnLeft(), turnRight(), and isTarget() API calls, while tracking visited positions and backtracking when blocked.

“The unknown room size is what got me at first.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Meta for a Data Scientist role, just one conflict-resolution question but they really dug into it.

Tell me about a time you had a conflict with someone at work. How did you handle it, and what would you do differently looking back?

“I went with a disagreement I had with an engineer over how we were defining a key metric.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026

Meta DS interview that leaned heavily on statistics fundamentals, more so than I expected. The whole thing revolved around a single scenario about comment counts per user, and they kept drilling deeper from every angle. Left feeling like I probably over-explained some parts and under-explained others.

Given daily comment counts per active user, define and compute the mean, median, and 95th percentile for a single day. Write out explicit formulas, including how you derive P95 from a sorted empirical distribution.
Explain the Central Limit Theorem and why the sampling distribution of the sample mean tends toward normality. What conditions are required for it to hold?
If you draw 200 independent simple random samples of size n from the same day's user comment data, what value of n is sufficient for the sample mean to be approximately normal? And how does the variance of the sampling distribution change as n grows?
How do plots of the daily mean, median, and P95 each behave as sample size n increases, and what explains the pattern?
If comments per user increased week-over-week, give at least three plausible statistical or product explanations and describe how you would test each one.

“Felt solid on mean and median but stumbled a bit articulating P95 from an empirical distribution.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta SWE coding round with two problems back to back. One was pretty standard merge stuff, the other was a classic BFS/backtracking problem that I've seen before but still had to think through carefully under pressure.

Given three sorted integer arrays, merge them into a single sorted array with all duplicates removed.
Given a string with letters and parentheses, remove the minimum number of parentheses to make it valid, and return all distinct valid results.

“Pretty approachable if you've done merge sort variants before.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral question from a Meta interview asking you to reflect on a colleague or mentor you looked up to during an internship or job. Pretty standard culture-fit territory but it's sneakily asking what you actually value in engineers and teammates.

Think about a colleague or role model you genuinely admired during an internship or job. Who was it, why did you pick them, and what qualities stood out to you?

“I picked someone from a past internship who was just really good at giving feedback without making you feel stupid.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta coding round, pretty standard heap problem but the interviewer locked out quickselect which meant I actually had to think about the heap approach properly instead of coasting on my usual solution.

Given an array of 2D points, find the k closest points to the origin using Euclidean distance. You must use a heap-based approach; quickselect is off the table.

“I almost started down the quickselect path out of habit and had to course-correct fast when they said no.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta coding screen, one algorithmic question on BST preorder validation. Pretty clean interview, just needed to know your stack tricks.

Given an array of integers representing a preorder sequence, determine whether it could be a valid preorder traversal of some binary search tree.

“The O(n) solution clicked for me pretty quickly once I thought about what preorder actually guarantees.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026

Second coding round at Meta, which got a little weird when the interviewer realized his planned question was basically the same one I'd already done in round one. He pivoted on the fly to a friend recommendation system design. Kept me on my toes.

Design a Friend Recommendation system.

“The interviewer swapped to this mid-session after realizing his original question duplicated round one.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Meta data engineer interview with a meaty system design question around video streaming metrics. One question, pretty open-ended, felt like they wanted to see how you think about observability end-to-end rather than just knowing the right answer.

Pick a video streaming metric and design a full monitoring and diagnostic system for it: define the time-series view with appropriate granularity and smoothing, break it down by dimensions like device, network, and geography, add diagnostic visuals like percentile bands and funnels, describe how a user would drill down through the data, and call out which guardrail metrics you'd show alongside it.

“I went with rebuffering ratio because I had something to say about it.”

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