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Meta interview notes, page 78 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

May 2026
Interviewed for a product analyst role at Meta, got a classic engagement analysis question about social content. Pretty straightforward framing but there's a lot of depth hiding underneath if you're not careful.
  • For a social media app, how would you compare whether content from friends versus content from creators or strangers drives more user engagement?

“I started by trying to define engagement, which felt right, but I spent too long on that part and the actual analysis structure came out rushed.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a software engineer role. One question, but it had four parts and they actually wanted you to go deep on all of them, which I was not fully prepared for.
  • Tell me about a time you worked across multiple teams to ship something. Walk through which teams were involved and what each owned, how you kept everyone coordinated, a real disagreement you hit and how you resolved it, and what the outcome was.

“The four-part structure threw me off a little.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Short coding screen at Meta, just one question about string manipulation. Nothing fancy, felt more like a warmup than a real technical bar.
  • Given a string that may contain letters and other characters, return the absolute difference between the count of uppercase letters and the count of lowercase letters.

“Pretty straightforward.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta onsite coding round, one question but it came with teeth. The base problem was straightforward enough but they pushed into follow-ups pretty fast and I wasn't as ready for the verbal explanation part as I thought I was.
  • Given an array of daily stock prices, find the maximum profit from a single buy and sell. Follow-up: extend your solution to handle unlimited transactions, then at most k transactions.

“The base case I had no problem with, single pass tracking the running minimum and updating max profit as you go.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview centered on a single deep product case: should Messenger add P2P payments, and how would you measure it. The whole session was basically one question that kept branching into sub-questions, which I wasn't fully prepared for.
  • From a business perspective, is adding a P2P payments feature to Messenger worth pursuing? What business goals would it serve?
  • What success metrics would you track for the Messenger P2P payments feature?
  • What are the potential negative impacts or risks of launching this feature?
  • How would you monetize the P2P payments feature?
  • Design an experiment or beta test to measure the impact of the payments feature. What variants, sample size, and duration would you choose?
  • Which metrics would you use to determine whether the feature met expectations after the experiment?
  • How would you compare user engagement between beta participants and non-participants, for example using a difference-in-differences approach?
  • If a user in the control group somehow gains access to the payments feature, how do you handle that contamination risk in your analysis?

“I started with user growth and engagement, which felt right, but I kept second-guessing whether to frame it around Meta's ad revenue flywheel or treat payments as a standalone revenue stream.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding round, one problem the whole session. The question looked like a tree traversal thing but had enough wrinkles that I spent a good chunk of time just making sure I understood what they were actually asking before writing a single line.
  • Given a binary tree, return two sequences: (1) a left-side view ordered from the deepest level up to the root, and (2) a right-side view ordered from the root down to the deepest level. Each view captures the first node visible at each depth from that side. Solve it in O(n) time with O(h) auxiliary space, and be ready to explain both a DFS and a BFS approach.

“The two-views-in-one-problem thing tripped me up at first because I kept conflating them.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design round for a software engineer role. The interviewer seemed to be running this AI-Enabled format for the first time, which made the whole thing feel a bit chaotic before we even got to the actual question.
  • Design a food delivery platform like DoorDash, with emphasis on trade-offs around shopping cart persistence and real-time driver location updates.

“The setup took forever, like 10+ minutes of the interviewer fumbling with the environment, so by the time we got to the actual problem I was half-distracted.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Coding round at Meta for an ML engineer role. Just one problem but it was enough to keep me second-guessing myself for a while after.
  • Given the head of a linked list, remove the k-th node from the end and return the updated list.

“Classic two-pointer setup but I fumbled the off-by-one on the first pass.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
ML engineer screen at Meta, just one question about designing a recommendation feature. Pretty open-ended and I wasn't sure how deep to go on the ML side versus the product side.
  • How would you design the 'Top Picks' personalized recommendation feature for Netflix?

“I spent way too long on the obvious stuff like collaborative filtering and not enough time thinking about cold start or how you'd actually evaluate the feature.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

May 2026
Meta data science interview with a meaty product analytics question that blended experiment design, stakeholder communication, and launch decision-making all into one scenario. The kind of question where you can tell they're looking for someone who's actually shipped something risky before.
  • You're seeing mixed signals before a feature launch: some guardrail metrics are trending in the wrong direction (like rising unsubscribe rates) while mission-aligned metrics (like offline connections) look promising but are lagging. How do you set up pre-launch success criteria and stop-loss thresholds, and how do you explicitly handle the tension between team-level metrics like CTR or send volume versus company-level metrics like retention or time-on-site?
  • Walk through how you'd structure a decision memo that argues transparently for or against launching under adverse leading indicators. What does risk mitigation look like, how do you design a phased rollout, and what triggers an automatic rollback?
  • How would you communicate a launch recommendation to engineering and cross-functional partners when the metrics are telling conflicting stories and not everyone is going to be happy with your framing?

“This is where I spent most of my time and probably over-indexed on the mechanics of setting thresholds rather than actually talking through the trade-off logic.”

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