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Meta interview notes, page 50 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, product sense round focused on Facebook Groups. Just one question but it had a lot of surface area and I don't think I covered it well.
  • You're a PM for Facebook Group Travel on mobile. What would you build?

“I spent too long on the setup and not enough time actually proposing something concrete.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Went through a Meta SWE coding round that leaned heavily on grid traversal. Two problems back to back, both classic but with a specific twist Meta apparently loves. The multi-source BFS thing is real, they do care how you set it up.
  • Given a grid containing empty cells (INF), walls (-1), and gates (0), fill each empty cell with the distance to its nearest gate.
  • In a binary grid of 0s and 1s, find the area of the largest island made of 4-directionally connected 1s.

“My first instinct was to BFS from each gate separately, which works but is way too slow and they pushed back on it pretty fast.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Meta system design round focused entirely on one deep question about API design and validation strategy for a top-k frequency service. Dense, technical, and they clearly wanted to see how far you could go before running out of things to say.
  • Design the API and system-level input validation and error-handling strategy for a service that computes the top-k most frequent elements in an array. Cover the request/response schema, payload limits, k bounds, element domains, idempotency, auth, rate limiting, timeouts, error taxonomy, validation layers, partial-failure behavior, observability, and safeguards against malformed or poisoned payloads. Also describe how you'd test and monitor this in production.

“This question is basically six questions stapled together and they let you run with it for the whole session.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Did a behavioral round at Meta for a software engineer role. Pretty standard stuff, nothing too wild, and it seemed to go fine.
  • Tell me about a time you had a conflict with a teammate or colleague. How did you handle it?
  • Describe a project that had a significant impact. What was your role and what made it meaningful?
  • Walk me through an AI-related project you've worked on. What was your contribution and what were the outcomes?

“Went through a situation from a past project, kept it pretty grounded.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026
Meta MLE interview focused on designing a short-video recommendation system for Reels. The session was heavier on metric design and A/B testing than I expected, less about novel architecture and more about whether you actually understand how to evaluate what you build.
  • Design a personalized short-video recommendation system similar to Reels.
  • How would you define and structure the metrics for this recommendation system, from the north-star down to model objectives?
  • Walk me through how you would design an A/B test for a change to the ranking model.
  • What features would you use, and how would you handle social graph signals?
  • How do you address position bias in training data, and how would you monitor for training-serving skew?
  • How would you handle cold start for new users and new videos?

“The four-stage pipeline (retrieval, pre-rank, rank, re-rank) is basically assumed knowledge going in.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta ML engineer coding screen, just one question about array manipulation. Pretty standard stuff but worth knowing cold.
  • Given an unsorted array of integers, write a function that moves all zeros to the end while preserving the order of non-zero elements.

“Two-pointer approach works cleanly here.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview with a heavy stats and experimentation focus. The whole thing was essentially one long A/B testing case study with five sub-parts, which sounds manageable until you're mid-calculation and second-guessing your pooled proportion formula.
  • Two search models each served 100 users. Model A had 90 successes, Model B had 85. Using a two-sided test at alpha=0.05, state your null and alternative hypotheses, choose the right test, compute the test statistic and p-value, and conclude whether A outperforms B.
  • Compute a 95% confidence interval for the difference pA minus pB and explain what it means for practical significance, not just statistical significance.
  • What per-arm sample size do you need to detect a 5 percentage-point lift over an 85% baseline with 80% power at alpha=0.05? Walk through the formula and inputs.
  • You're running these two models across 10 independent intents simultaneously. Apply a Bonferroni correction and explain whether your conclusions change.
  • When would you prefer an exact test over a z-test, and when would you use a Bayesian comparison instead? What would you actually report in each case?

“Went with a pooled two-proportion z-test which felt right.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Meta product sense round, one question about launching a feature that's already live. Weird to prep for but it's a good way to see if you actually think in systems.
  • How would you design and launch the reactions feature for Facebook?

“I knew this feature existed so I kept second-guessing whether they wanted me to reverse-engineer the real launch or treat it as a blank slate.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Meta SWE interview with a multi-level coding assessment. This was level 2 of 4, building on top of a work-hours tracking system from the previous round by adding a ranking query on top of it.
  • Extend a work-hours tracking system to support a `top_n_workers(n, position)` method that returns the top N workers for a given position, ranked by total accumulated hours descending, with ties broken by worker ID lexicographically. Return results as formatted strings like `"<worker_id>(<total_time>)"`.

“The sorting part was fine, the annoying bit was remembering to handle the case where fewer than n workers exist for a position without crashing or returning garbage.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
A product-meets-data-science question at Meta where they basically asked me to design a full recommendation feature from scratch, including the algorithm, the UX, and how to measure it. Pretty broad scope for one question.
  • A food delivery app wants to add a 'Restaurants You May Know' section to the home page. Why would users and the business want this, how would you design the recommendation algorithm and the user experience end to end, and how would you measure whether it actually worked?

“This felt like three questions crammed into one and I kind of froze deciding where to start.”

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