← Back to Directory

Meta interview notes, page 205 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

Jun 2026
Meta PM interview with a single product design question centered on building a volunteering product for a startup. Pretty light on content but the scenario itself was interesting enough to think through.
  • You are a PM at a startup building a volunteering product. How would you approach designing it?

“I went straight into defining the user and their core problem, which felt right, but I spent too long on the nonprofit side and kind of forgot about the volunteer themselves.”

View Post

Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a product analytics focus. The whole session was basically one meaty scenario about push notifications, which sounds narrow until you realize how many directions it can go.
  • What metrics would you track to evaluate the quality of push notifications in a consumer app, and how would you define thresholds for what counts as 'high quality'?
  • Design an experiment to evaluate a new push notification algorithm at the time of its launch.

“I went straight to open rate and click-through, which felt obvious the moment I said it out loud.”

View Post

Meta·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed at Meta for a PM role, just one question about past experience and fit. Pretty short interaction, not much to go on.
  • Walk me through your past experience and explain why it makes you a strong fit for a PM role.

“Classic opener but I still fumbled it a bit.”

View Post

Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round with a maze manipulation problem. Pretty focused on getting the edge cases right around preserving special characters on the path.
  • Given a 2D character grid representing a maze with start (S), end (E), walls (#), and open cells (.), and a precomputed path through it, modify the grid so that open cells on the path become *, but S and E stay as-is. All other cells remain unchanged.

“The naive solution just stamps * on everything in the path list, which breaks S and E.”

View Post

Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Meta MLE onsite focused almost entirely on a single deep system design problem around location recommendations. It was one of the more exhaustive design sessions I've had, covering pretty much every layer of the stack from data ingestion to serving.
  • Design a location and place recommendation system from scratch. Walk through how you'd acquire data, build features, generate candidates, rank them, and serve results at scale.

“This question ate the whole session.”

View Post

Meta·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
PMM interview at Meta with a product strategy question about structuring a new feature opportunity and building a go-to-market plan around it. Pretty open-ended, which I was not fully prepared for.
  • How would you structure the opportunity for a new product feature and build a go-to-market strategy around it?

“I started with market sizing and worked toward positioning, but the go-to-market piece felt rushed because I spent too long on the opportunity framing.”

View Post

Meta·Data Scientist·Technical Phone Screen

Jun 2026
A technical deep-dive at Meta for a DS role, basically one giant question about clustering users at scale. The whole thing was a system design exercise dressed up as an ML question, and it covered way more ground than I expected.
  • You have user feature data, a social graph, or both. Walk through how you'd cluster users to find meaningful groups like communities or interest segments, covering algorithm choice, preprocessing, distance metrics, and how you'd pick the number of clusters.
  • If the main data source is a social graph rather than a feature table, what community detection approaches would you use and how does that fundamentally differ from clustering a feature matrix?
  • How do you handle directed and weighted edges in graph clustering?
  • How would you combine user feature data and graph structure when both are available?
  • What metrics and validation strategies would you use to evaluate cluster quality, both for feature-vector clustering and for graph-based community detection?
  • What practical problems come up when running user clustering at millions of users, and how would you address things like compute constraints, dynamic graphs, cold-start users, and cluster drift over time?

“This is where I spent most of my time.”

View Post

Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta software engineer interview covering a mix of stack-based simulation, interval scheduling, tree traversal, and selection algorithms. Pretty standard Meta coding lineup if you've seen their question pool before.
  • Given a list of function call logs with start and end timestamps, calculate the exclusive execution time for each function.
  • Given an array of meeting intervals, find the minimum number of conference rooms required to hold all meetings.
  • Given a binary tree, return its nodes grouped by vertical column order from left to right.
  • Find the Kth largest element in an unsorted array.

“Stack problem but the off-by-one stuff in the time calculation tripped me up.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview, one product design question about farms. Pretty open-ended and I wasn't totally sure where they wanted me to go with it.
  • Design a product for farms.

“I froze for a second because it's such a weird space for Meta to be asking about.”

View Post

Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Meta for an ML engineer role. One question, but it had real teeth to it.
  • Tell me about a project that failed. Walk through what it was trying to accomplish, what failure actually looked like, the root causes, your specific role, and what you'd do differently. Also, where have you applied those lessons since?

“This one is deceptively hard because the instinct is to pick something that sounds like a failure but was secretly fine.”

View Post