← Back to Directory

Meta interview notes, page 131 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
Product strategy round at Meta, three scenarios back to back. Each one asked you to define an MVP and a go-forward strategy, which sounds manageable until you're juggling three very different domains in one sitting.
  • Design a product strategy and MVP for a peer-to-peer lending and borrowing platform. Who are you building for, what's the core value, how do you measure success, and what are the rollout risks?
  • Build a product strategy and MVP for a service that helps travelers make use of airport layovers. Define the target user, value proposition, success metrics, and key risks.
  • Propose a product strategy and MVP for a new AR/VR product coming out of Meta's hardware lab. Same structure: users, value prop, metrics, risks.

“I spent too long on the user segmentation part and kind of rushed through risk.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a product execution question about changing where the like button lives on photos. Pretty focused session, just the one question but it had some real depth to it.
  • How would you execute the change of moving the 'like' button on photos into a menu hidden behind a three-dot icon?

“My first instinct was to jump straight into rollout phases but I kept second-guessing whether they wanted execution mechanics or the product reasoning behind the change.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
Product analytics case for Meta DS, focused entirely on a group calling feature launch. Four connected questions that built on each other, which I wasn't expecting. The network effects angle at the end is where things got uncomfortable.
  • What survey questions would you design to uncover user needs around a group calling feature, for both current and potential users?
  • Without direct access to competitor data, how would you benchmark how widely group calls are being adopted on competing platforms?
  • Design an experiment to measure the causal impact of launching group calls. Define your treatment and control groups, your primary metric, and any guardrail metrics. Also explain whether total call volume is the right primary metric.
  • Group calling has strong network effects, which makes standard A/B testing prone to spillover. If a cluster-randomized experiment isn't feasible due to engineering constraints, what alternative approaches could you use to measure causal impact?

“I started with feature importance ratings and willingness to pay, which felt solid.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product sense round at Meta focused on feed ranking and signals. Just the one question but it had a lot of depth to it.
  • What signals would you use to improve the relevance of Meta's News Feed?

“I started rattling off engagement signals like likes and comments and shares, and then realized pretty quickly that felt too surface-level.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a product design question about building a babysitter service on Facebook. Pretty open-ended, which I wasn't totally prepared for.
  • How would you design a babysitter service for Facebook?

“I went straight into features and kind of skipped over who the actual user is, which I regret.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview with a probability and stats question centered on ad impression modeling. Pretty focused, just the one problem but they pushed on the math.
  • You have Y ad impressions distributed randomly across X users. What is the expected number of impressions per user, and what is the probability that any given user sees at least one impression?

“The expected value part is straightforward, just Y divided by X.”

View Post

Meta·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a Data Scientist role. Four questions, all pretty standard leadership and interpersonal stuff. Nothing technically deep but I definitely fumbled a couple of them more than I expected to.
  • Tell me about a challenging project you led that had a significant impact.
  • Describe a situation where you had to manage or resolve conflict within your team.
  • Give an example of a time you delivered constructive feedback to someone.
  • How have you helped a new team member get up to speed?

“I had a story ready but rambled way too long on the setup and barely got to the outcome before they nudged me along.”

View Post

Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral screen for a SWE role at Meta. Just one question but it's the kind that can go sideways fast if you don't have a tight story ready.
  • Tell me about a project you found particularly challenging and what specifically made it hard.

“I had an answer ready but it felt too clean in hindsight, like I was describing difficulty without actually conveying struggle.”

View Post

Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design round for an EM role, one question, pretty focused. The problem sounds deceptively straightforward but there's a lot of surface area once you start pulling on the threads.
  • Design a system that surfaces the top 10 most frequently played songs over the last 7 days.

“I went straight to a sliding window approach with a distributed counter store, which felt right, but I fumbled when they pushed on consistency vs.”

View Post

Meta·Data Scientist·Technical Phone Screen

May 2026
Product analytics case for a Data Scientist role at Meta. The whole thing was a single deep-dive into Stories usage across Instagram and Facebook, and it went longer than I expected with a bunch of follow-ups layered on top.
  • Instagram Stories has a much higher usage rate than Facebook Stories, and the gap has been stable over time. How would you diagnose and quantify why?
  • What data validation or sanity checks would you run before trusting the metric comparison between the two apps?
  • How would you break down the usage gap into components to figure out where it's actually coming from?
  • What product or audience hypotheses would explain why IG has higher Stories adoption than Facebook?
  • What experiments or product changes would you test to improve Facebook Stories usage, and how would you measure success?
  • How would you handle novelty effects or seasonality when evaluating whether an experiment on Stories actually worked?

“This is the kind of question where you can spiral fast if you don't slow down and structure it.”

View Post