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Meta interview notes, page 54 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·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, just one algorithm problem the whole session. Pretty standard grid traversal stuff but I fumbled some of the edge case thinking out loud.
  • Given an n x n grid where cells are either open or blocked, find the length of the shortest path from the top-left corner to the bottom-right corner. Return -1 if no path exists.

“Knew it was BFS pretty fast, that part wasn't the issue.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview with a banking system design problem. The Level 1 API question felt straightforward at first but there were enough edge cases to keep you honest.
  • Implement a basic banking system API with three operations: creating an account, depositing funds, and transferring funds between accounts. Each operation takes a timestamp and must handle invalid inputs by returning appropriate values (False or None).

“The create and deposit parts went fine.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta ML engineer screen where they had me implement linear regression from scratch, closed-form solution and all. Pretty math-heavy for a coding round, but they explicitly said AI assistance was fine which was a nice twist.
  • Given a set of (x, y) points, implement linear regression by hand using the closed-form solution that minimizes mean squared error. Derive the gradient of MSE with respect to the weights, set it to zero, and code up the resulting weight expression. Then verify your implementation numerically against gradient descent on a small synthetic dataset.

“The derivation part was fine, I remembered enough calculus to get through it.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta data engineer interview with a graph/social network traversal problem. Pretty standard algorithmic question but the specifics tripped me up a bit in how to handle deduplication cleanly.
  • Given a follow graph represented as a dictionary (e.g. {A:[B,C], B:[C,D], C:[E]}), find all accounts that a user's followees follow, excluding anyone the user already follows themselves. No duplicates, order doesn't matter.

“My first instinct was to just flatten everything and filter, which works but I kept second-guessing myself on the dedup step.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview with a single product design question about building a therapist search app. Pretty open-ended, which I wasn't expecting from a company that usually goes deep on metrics.
  • Design an app that helps users find therapists.

“I spent the first couple minutes just trying to figure out who the user even is, someone in crisis, someone casually browsing, a parent looking for a kid's therapist.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for an infrastructure engineering role. Four questions back to back, all the classic ones you'd expect, but they move fast and expect real specificity.
  • What project are you most proud of, and what made it stand out to you?
  • Tell me about a time you had a conflict with a peer or stakeholder. How did you handle it?
  • Describe a project where the goals or requirements were unclear. How did you move things forward?
  • Share a piece of feedback you received from a peer or manager and explain how you acted on it.

“I had a solid story here but rambled a bit on the technical details when I should've stayed on the impact side.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview with a meaty experiment design question around a new Instagram Shopping feature. One question but it had a lot of layers to it, felt like they were testing whether you'd actually worked through a real launch before.
  • Instagram is rolling out an in-app Shopping feature. How would you design an experiment to measure its impact on user engagement and revenue? What metrics and success criteria would you track, and how would you handle selection bias or network effects?

“This is the kind of question where you think you're doing fine and then the follow-ups expose all the stuff you glossed over.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta system design round focused entirely on building a trending hashtags service at Facebook scale. Three parts, one hour, and by the end I felt like I'd only scratched the surface of what they actually wanted.
  • Design a backend system that detects and serves trending hashtags on a large social network. The system needs to ingest posts continuously, compute trend scores, and serve results with roughly one minute of end-to-end latency.
  • How would you operationalize 'timeliness', 'popularity', and 'novelty' into a single computable trend score that refreshes at least once per minute?
  • How do you handle the read path so that the trending list is served at very high QPS with under 100ms latency?
  • How would you extend this design to support personalized or per-region trending lists without re-aggregating the entire firehose for every segment?
  • A coordinated group floods a new hashtag from many fake accounts to manufacture novelty and popularity signals. What defenses would you build into the trending system?
  • How would you normalize and cluster near-duplicate hashtags like #WorldCup and #world_cup so they count as one trend?
  • How would you measure trend quality both offline and online, and how would you tune the scoring weights over time?

“The scope of this one is enormous.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Meta for a software engineer role. The whole session was basically one massive question about building a distributed config service, and they kept pushing deeper into every layer. Brutal but fair.
  • Design a globally distributed key-value configuration service that serves thousands of microservices. Walk through your key schema design including namespacing, versioning, and composability, as well as your value schema covering serialization format, validation, and schema evolution.
  • How would you design the partitioning and sharding strategy, indexing approach, and storage engine for this config service? Compare LSM-tree vs B-tree tradeoffs and explain the read and write paths, including how you'd handle hot keys.
  • What consistency model would you choose for this service, and how would you handle leader election, replication quorums, network partitions, and failover?
  • How would you support transactions, watch/subscribe functionality for live config updates, TTLs on config entries, and backup and restore for this service?
  • How would you approach capacity planning, define SLAs, design multi-region replication, and test a system like this end to end?

“I started with namespacing because it felt like the safest entry point, something like service/environment/feature_flag as a hierarchical key.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta PM interview with a product strategy question about Instagram's shopping feature. Pretty focused session, just one meaty question but it had a real teeth-to-it follow-up that I didn't fully see coming.
  • How would you define goals and success metrics for Instagram Store, and how would you handle a situation where purchases go up but engagement drops in other parts of the app?

“The first part I handled okay.”

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