← Back to Directory

Meta interview notes, page 113 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
PM interview at Meta where they threw a Spotify scenario at me, asking me to reason through a product strategy decision and tie it back to metrics. Pretty classic case-style question dressed up with a specific company context.
  • You're a PM for Spotify Podcasts. Why did Spotify add podcasts to its platform, and why did that happen before audiobooks? What metrics would you use to track the success of that product decision?

“Three questions in one, which I didn't fully register until I was already mid-answer.”

View Post

Meta·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a software engineer role at Meta, mostly coding-focused. The problems ranged from classic sliding window stuff to some trickier string manipulation questions that felt a bit obscure. Mixed bag overall.
  • Implement a moving average from a data stream, given a fixed window size.
  • Group a list of strings such that strings that are shifts of each other end up in the same group.
  • Given a sorted integer array and a range, return all the missing ranges within that range.
  • Remove duplicate characters from a string while preserving the original order of first appearances.
  • Given an array, return its elements in sorted order while maintaining stability and minimizing time/space complexity.

“Pretty standard sliding window with a queue.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview with a product analytics angle. The main question was about building a fake-user classifier and picking the right evaluation metrics, which sounds straightforward but turns into a whole conversation about business tradeoffs pretty fast.
  • You're seeing a spike in daily average comments that might be driven by fake users. You need to build a classifier to detect them. What evaluation metrics would you use and why?

“I started with F1 and ROC-AUC because those felt safe, but the follow-up pushed me on the actual business cost side and that's where I got a bit wobbly.”

View Post

Meta·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a Data Scientist role at Meta. Three questions, all the classic conflict-feedback-ambiguity trio. Nothing technically surprising but the depth they wanted caught me a little off guard.
  • Tell me about a time you disagreed with a teammate and how you resolved it.
  • Describe a project where the goal was unclear and explain how you brought clarity to it.
  • Give an example of receiving critical feedback and describe what you did with it.

“I had a decent story ready but fumbled the ending.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a classic product design question about airport layovers. Pretty open-ended, which I wasn't fully ready for.
  • How would you improve the airport layover experience?

“I went straight to traveler pain points and started listing stuff like long walks between gates, bad food options, nowhere to sleep.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
A technical screen for a Data Scientist role at Meta focused on stats fundamentals, specifically around building confidence intervals and understanding sampling distributions in the context of detecting fake accounts from comment data.
  • Given a sample of comment counts per user, how would you construct a 95% confidence interval for the population mean, and what assumptions does this require?
  • What is the sampling distribution of the sample mean, and how does sample size affect its variance?
  • How do you compute and interpret the 95th percentile of a comment count distribution?

“I walked through the standard formula, sample mean plus or minus 1.96 times standard error, and mentioned the Central Limit Theorem as the justification for assuming normality of the sample mean.”

View Post

Meta·Technical Program Manager·Onsite - System Design / Architecture

May 2026
Meta TPM interview with a backend system design question focused on user data portability. Pretty open-ended, which I wasn't fully prepared for.
  • Design the backend system that lets users download all of their data from their account.

“I jumped straight into the data export flow without thinking about scale first, which I think hurt me.”

View Post

Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta SWE coding round, got a matrix manipulation problem. Pretty standard stuff but the in-place constraint is where people usually trip up.
  • Given an m x n integer matrix, if any element is zero, set its entire row and column to zero. Return the modified matrix.

“The naive approach bites you immediately if you start zeroing things out as you go, because you lose track of which zeros were original.”

View Post

Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Coding round at Meta for a Software Engineer role, two algorithmic problems back to back. Nothing behavioral, just straight into the problems. Tree stuff and matrix traversal, which is pretty standard Meta territory from what I've heard.
  • Given an array of distinct integers representing the preorder traversal of a BST, reconstruct the original tree and return its root. A linear time solution is expected.
  • Given an m x n integer matrix, return all elements visited in clockwise spiral order, peeling off the outer boundary layer by layer until the whole matrix is covered.

“I knew preorder means root comes first, so that part clicked fast.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview focused entirely on a product metrics and experimentation case around push notifications. One big question, lots of moving parts, and I definitely underestimated how deep they wanted to go on the stats side.
  • A mobile app sends push notifications and wants to ensure only high-quality notifications are shipped. Propose and justify a metric for notification quality, explain how you'd use historical CTR data to establish whether current performance is good or bad, and design an A/B test for a new notification strategy including setup, success metrics, and in-depth result analysis.

“This question ate up the whole session.”

View Post