← Back to Directory

Meta interview notes, page 149 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 product design question about building a Netflix-style streaming experience specifically for kids. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a streaming video product tailored specifically for children?

“I jumped straight into features and the interviewer kind of just waited.”

View Post

Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a brutally detailed causal inference question about measuring brand advertising effectiveness on social media. One question, but it had like nine sub-parts and I'm pretty sure I was still answering it when they cut me off.
  • You're given the hypothesis that social media advertising is less effective for brand building than other channels. You have historical multi-channel data and can run experiments. Walk through a full causal measurement plan: how you'd define brand outcomes, what experiment designs you'd consider, how you'd power the test, what model you'd pre-register, how you'd handle budget confounding, what guardrails you'd set, how you'd approach subgroup analysis, and what your final decision rule would look like.

“This was one of those questions where you finish your first answer and realize you've only covered maybe a third of what they actually wanted.”

View Post

Meta·Data Scientist·Onsite - Multi Round

Jun 2026
A Meta DS onsite that was basically one giant experiment design question stretched across five sub-parts. The kind of question where you think you've covered everything and then they hit you with 'but what about interference' and you realize you haven't.
  • Your team is launching a Group Calls feature in an app that currently only supports 1:1 calls. Pick one primary metric and 3 to 5 guardrails for measuring its impact. Also explain when total call volume is a misleading metric and when it might actually be fine to use, and propose rate-based or user-normalized alternatives.
  • How would you choose a randomization unit for this experiment, and what rollout plan would you use to reduce network interference? Compare graph cluster randomization, ego-clusters, and geo-based switchbacks on bias, variance, and engineering cost.
  • If cluster-based randomization isn't feasible, what quasi-experimental design would you use instead? Walk through the assumptions and diagnostics you'd run.
  • How do you define exposure and triggers for this experiment? What are your exclusion criteria, and how do you prevent contamination between treatment and control groups?
  • How would you pre-register this analysis? Specify your minimum detectable effect given plausible baselines, how you'd handle seasonality, and what you'd do about novelty and ramp-up effects.

“I went with call success rate per active user as my primary metric.”

View Post

Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one problem the whole session. They took a classic BFS maze and layered on keys and locked doors, which sounds manageable until you realize your state space just tripled in complexity. Felt okay about my solution but not great.
  • You have a working BFS maze solver on a 2D grid with walls, open cells, a start S and end E. Now extend it: the grid has keys (lowercase letters) and locked doors (uppercase letters). You can only pass through a door if you already hold the matching key. Design a state-augmented BFS where each state tracks position plus which keys you currently hold, and find the shortest path from S to E.

“My first instinct was to just track visited cells like normal BFS and I immediately started coding that before catching myself.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a single product design question about grocery shopping. Pretty open-ended, which sounds fun until you're actually sitting there trying to figure out where to start.
  • How would you design a better grocery shopping experience?

“I went straight to the app layer and started talking about personalized lists and predictive reordering.”

View Post

Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE interview, got the cloud storage design question which goes pretty deep across four levels. Each level adds a new layer of complexity and the later ones have some real gotchas if you're not thinking carefully about state.
  • Design a basic cloud storage system with add_file, get_file_size, and delete_file operations.
  • Extend the storage system to support a get_n_largest operation that returns the N largest files filtered by a given name prefix, sorted by size descending and then by name ascending on ties.
  • Add user accounts to the storage system. Each user has a storage capacity limit. Implement add_user, add_file_by (which fails if the upload would exceed the user's capacity), and merge_user (which combines two users, summing their capacities and transferring all files to one user).
  • Add backup and restore functionality for users. Each user can have at most one backup snapshot. Restoring a user overwrites their current state entirely. How do you handle filename conflicts when restoring if another user has since claimed one of the backed-up filenames?

“Pretty straightforward as a warmup.”

View Post

Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta coding screen for a software engineer role, one question the whole time, pretty focused on the data structure angle rather than just getting a working solution.
  • You have two arrays and a sequence of operations: one operation type updates a value in the second array at a given index, the other asks how many index pairs (i, j) satisfy primary[i] + secondary[j] == some target. Return results for all query operations in order. How do you handle this efficiently?

“My first instinct was brute force the pair count each time, which is obviously bad.”

View Post

Meta·Machine Learning Engineer·Onsite - Product Sense / Strategy

Jun 2026
Meta ML engineer interview with a product design question that felt more PM than engineering. One question, fairly open-ended, and I kept second-guessing how technical to go.
  • How would you design the Explore page for Instagram?

“I spent the first few minutes rambling about user intent before I even touched the ML side, which was probably the wrong call for this role.”

View Post

Meta·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Meta MLE interview, came down to a coding round with a prefix sum problem. Nothing too wild but the variant twist is what gets you.
  • Given an integer array and a target value k, find the total number of contiguous subarrays whose elements sum to k.

“Knew this problem but the variant tripped me up a bit.”

View Post

Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding question, just one problem about finding the class with the most students across two consecutive years. Pretty focused session, nothing behavioral from what I can tell.
  • You're given a list of classes, each with a name, size, start year, and end year. Find the maximum number of students attending across any two consecutive years.

“This one took me a minute to even parse what 'two consecutive years' meant in context.”

View Post