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Meta interview notes, page 87 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

May 2026
PM interview at Meta, one question about measuring success for Reels. Pretty focused session, no fluff.
  • How would you define and measure success for Instagram Reels?

“I went straight for engagement metrics and the interviewer kind of waited, like they wanted more.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round, one problem the whole time. Classic tree manipulation question but the circular linking part tripped me up more than I expected.
  • Given the root of a binary search tree, convert it in place to a sorted circular doubly linked list. Left pointers serve as predecessors, right pointers as successors, and the list wraps around so the smallest and largest elements point to each other. Return the pointer to the smallest element.

“I knew in-order traversal was the right move pretty fast.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Meta for an EM role, just one question the whole time. Pretty standard cloud storage prompt but there's more to it than people expect.
  • Design a file storage and sync service like Dropbox.

“Spent the first few minutes on the obvious stuff, chunking files, metadata storage, sync conflicts.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta coding screen, one algorithmic problem, nothing fancy but it'll trip you up if you don't think carefully about the ordering logic.
  • Given a set of numbers, arrange them to form the largest possible number.

“Looks dead simple until you realize sorting by value doesn't work.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a single very involved causal inference question about estimating the effect of a free employee shuttle program across hundreds of sites. The whole thing was basically a 45-minute deep dive into one problem, which I was not fully prepared for in terms of breadth.
  • You have site-level data from 1,000+ locations, several hundred of which adopted a free employee shuttle at different points in time. Design a full causal analysis to estimate the shuttle's effect on employee participation and engagement, covering your estimand, identification strategy, parallel trends checks, confounders, clustering, how you handle never-adopters and late adopters, robustness checks, and how you'd communicate results to non-technical stakeholders.

“This question basically ate the entire interview.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Went through a product sense round at Meta for a PM role. Single question, product goals framing, pretty standard but I fumbled the structure more than I expected.
  • If you were the product manager for a food delivery platform like Uber Eats, what goals would you set?

“I jumped straight into metrics without anchoring on what phase the product was in.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Interviewed for a PM role at Meta, got a product metrics question about the news feed. Pretty standard for Meta but still tripped me up a bit.
  • How would you measure the success of a personalized news feed?

“I went straight to engagement metrics and kind of dug myself into a hole.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Meta MLE technical screen, one coding problem the whole time. Pretty focused on getting the algorithm right and then picking apart edge cases until I wanted to cry.
  • Given an array of positive integers and a target count k, you can cut each element into pieces of any positive integer length L. Find the maximum L such that the total number of pieces is at least k. Return 0 if it's not possible. Your solution should run in O(n log max(lengths)) time, and you need to handle integer overflow and edge cases explicitly.

“Binary search on L was the obvious move but I fumbled the predicate setup for longer than I'd like to admit.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a Product Design Engineer role. Two questions, both focused on how you work with people rather than anything technical. Pretty standard for this type of screen but the conflict question had some teeth to it.
  • How do you communicate across cross-functional teams like engineering, design, product, and external partners? Give a specific example where you had to bridge a communication gap.
  • Describe a conflict you had with a partner, peer, or stakeholder. What was the disagreement, how did you handle it, and what happened in the end?

“I had a decent story ready for this but I think I rambled too long on the setup and didn't get to the actual gap-bridging part until the interviewer nudged me.”

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Meta·Data Scientist·Onsite - Multi Round

May 2026
Brutal Meta DS onsite focused entirely on a News Feed experiment design scenario. Seven sub-questions, all technical, all requiring you to actually know the math. Not a vibe check at all.
  • How would you choose the experimental unit and randomization scheme to reduce interference in a News Feed ranking experiment, given a 10% treatment cap and a highly skewed degree distribution?
  • Define the direct effect on consumers, the spillover effect, and the total effect as precise estimands. What exposure model makes these identifiable?
  • Walk through how you'd compute point estimates and 95% confidence intervals using cluster-robust or randomization-inference standard errors. What assumptions are you making and what breaks if they're violated?
  • Derive the required sample size under clustering with an average cluster size of 120 and ICC of 0.02. Use the design effect formula to find the effective sample size needed for 80% power to detect a 0.6% relative lift, given a baseline mean of 4.0 sessions per user and SD of 5.5.
  • Propose at least three concrete diagnostic checks to detect leakage or interference in the experiment, and explain how you'd respond to each finding.
  • Which sequential testing method would you use for interim looks every two days, and what are your stopping and continuation rules to maintain Type I error control?
  • If leadership forces a standard 50/50 user-level randomization instead of clustering, what post-hoc approaches would you use to bound the bias and produce conservative confidence intervals?

“This is where I spent most of my mental energy.”

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