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Meta interview notes, page 51 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

Apr 2026
Meta PM interview with a single product design question about building a podcast feature into Facebook. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a podcast app for Facebook?

“I went straight into user segments and almost forgot to ask clarifying questions first, which is such a basic mistake.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta EM interview, one system design question and that was basically it. Pretty standard for the role but still a lot to cover in a single session.
  • Design a video sharing and streaming platform similar to YouTube.

“There's a lot of surface area here and I think I spread myself too thin early on.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Short product strategy question for a data science role at Meta. Not much to report in terms of process but the question itself was interesting enough to think about.
  • Would you bring Facebook Rooms over to Instagram, and how would you think about that decision?

“I went straight to user overlap and whether the core use case even fits Instagram's vibe.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview with a multi-level OOP/design problem. Level 1 was about building a work hours tracking system, which sounds straightforward but has a few subtle edge cases worth thinking through carefully.
  • Design and implement Level 1 of a WorkHoursRegister system that tracks when workers enter and exit an office. You need to support adding workers with an ID, position, and hourly compensation; a register call that toggles their in/out status and records completed sessions; and a get call that returns total accumulated work time from completed sessions only.

“The toggle mechanic is where I had to slow down.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview focused entirely on experiment design for an ads ranking model. Four follow-up questions all built on each other, which I wasn't expecting. The segmentation question in particular made me realize I hadn't thought carefully enough about heterogeneous effects before walking in.
  • How would you design an experiment to evaluate a new ML-based ad recommendation system, and which primary and guardrail metrics would you choose?
  • An A/B test shows a 5% lift in CTR. How do you determine whether that's practically significant, not just statistically significant?
  • CTR doubles specifically for Indian males aged 18 to 55 in your experiment. What could explain this, and what would you do next?
  • If the experiment shows both CTR and revenue up 5%, would you roll out the new model globally? Walk through your decision process.

“I started with randomization unit (user-level) and sample size, which felt right, but I fumbled a bit when justifying guardrail metrics.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Meta for a SWE role, centered entirely on building an online auction platform. Pretty deep dive into concurrency and real-time update mechanics. The interviewer had a clear checklist in mind and accepted the answer basically the moment three specific concepts were named.
  • Design an online auction system with real-time bid updates, concurrent bidding, and proper end-of-auction semantics.
  • How would you handle real-time bid notifications to clients, and what are the trade-offs between SSE, WebSockets, and long polling?
  • How do you prevent a race condition when multiple bids arrive simultaneously right before an auction closes?
  • Walk me through how you'd estimate QPS for this auction system.

“The whole interview was basically this one question with layers peeled off one by one.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for a software engineer role. Just one question about receiving constructive feedback from a manager, nothing too wild but it made me think harder than I expected.
  • Tell me about a time you received constructive feedback from a manager. How did you respond to it?

“I had a story ready but it felt a little thin once I started telling it.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview focused entirely on the linked list deep copy problem, which sounds straightforward until they start peeling back layers asking about space optimization and correctness guarantees. More depth than I expected for what looked like a classic problem.
  • Given a singly linked list where each node has a `next` and a `random` pointer (which can point to any node or be null), construct a complete deep copy of the list and return its head.
  • Walk through the hashmap-based approach: how does it work, and what are its time and space complexities?
  • Can you solve this in O(1) auxiliary space? Describe the interleaving technique where you weave clones into the original list, set random pointers, then separate the two lists.
  • What happens when random pointers form cycles or point backward in the list? Does your algorithm still work correctly?
  • How would you verify that the deep copy is structurally correct, including that no copied node's pointer accidentally references a node from the original list?
  • What edge cases does your solution need to handle? Think about an empty list, a single node whose random points to itself, and a list where all random pointers are null.

“I jumped straight to the hashmap solution because it's the cleanest to explain under pressure.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE interview focused on a streaming top-K frequency problem, covering both exact and approximate solutions. Pretty interesting problem once you get into it, though the complexity analysis portion felt like it had more depth than I expected for a single question.
  • You're receiving a continuous stream of items that may not fit in memory. How would you design an algorithm to find the top K most frequent items? Cover both an exact approach when distinct items fit in memory and an approximate approach when they don't, and walk through the complexity of each.

“This one took me a second to get my footing.”

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Meta·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a bizops role at Meta, got asked about bridging the gap between technical and non-technical teams. Pretty standard cross-functional stuff but worth thinking through before you walk in.
  • How do you communicate technical project needs to non-technical teams?

“I rambled a bit about translating jargon and using analogies, which felt thin the moment I said it.”

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