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Meta interview notes, page 194 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

Jun 2026
Meta SWE coding round, got a tree traversal problem that's basically a classic LeetCode variant. Pretty standard stuff but there are two approaches and I fumbled explaining the tradeoffs between them.
  • Given the root of a binary tree, return the values of the nodes visible when looking at the tree from the right side (or left side), ordered top to bottom.

“Knew this one the second they said 'binary tree' and 'visible nodes'.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta Research Engineer coding screen, one algorithmic problem the whole time. Pretty standard graph/island-style question but the twist kept me thinking longer than I expected.
  • Given an n x n binary matrix, you can flip at most one 0 to a 1. What is the size of the largest island you can form? An island is defined as a group of 1s connected in four directions.

“My first instinct was brute force: try flipping every 0 and run BFS each time.”

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Meta·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
Meta data scientist interview focused entirely on a location-based restaurant recommendation feature, going deep on data requirements, model validation, and privacy tradeoffs. Seven questions, all connected to the same scenario, which made it feel more like a case study than a traditional interview loop.
  • How would you use real-time location data from a nearby friends feature to build a new product feature?
  • What's the rationale for building restaurant recommendations, and how might user behavior shift as a result?
  • What datasets would you need to power this recommendation system?
  • How would you validate whether the recommendation model is actually working?
  • After launching the feature, which metrics would you track to measure success?
  • What negative consequences could this feature introduce?
  • How does building a restaurant recommendation system differ from a people recommendation system like friend suggestions?

“I leaned into live coordinates and activity patterns pretty quickly but forgot to tie in the social graph until they nudged me.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview with a meaty experimentation question about Messenger. The whole thing hinged on network effects and whether you actually know how to handle interference in an A/B test, not just recite the standard setup.
  • You're running an A/B test for a new Messenger feature that may have network effects. How do you choose the randomization unit, justify that choice, and what statistical approaches would you use to detect and reduce interference between users?

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

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview focused entirely on the metrics and experimentation side of launching a short-video recommender feed. Four connected sub-questions that built on each other, which I didn't fully anticipate going in.
  • If you had to pick a single metric to evaluate whether Instagram's short-video recommendation system is working, what would it be and why?
  • Sketch the expected distribution of your chosen metric. Label the median, mode, and 95th percentile.
  • Suppose your primary metric goes up but a secondary metric drops at the same time. How do you handle that tradeoff?
  • Walk me through the full A/B testing process you'd use to launch this recommender feed, from setup to decision.

“I went with watch time pretty quickly and then had to defend it against retention.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Meta for a software engineering role. The whole session was basically one big question about building a real-time 1-to-1 messaging system, and they pushed hard on every layer of it.
  • Design a 1-to-1 chat application at the scale of something like WhatsApp or Messenger, covering real-time messaging, presence, read receipts, push notifications, and message history.

“I started with WebSockets vs SSE and the interviewer let me run with it for a while, which felt like a good sign.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round, one algorithmic question on trees. Pretty standard stuff but worth knowing cold if you're prepping for this loop.
  • Given a binary tree and two nodes, find their lowest common ancestor.

“Classic tree problem.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Meta behavioral round for a senior software engineer role, focused heavily on technical leadership scope and cross-team influence. One question but it had a lot of weight behind it.
  • Tell me about a time you set the technical direction for something that had broad organizational impact.

“The part that trips people up is the scope check.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta PM interview with a product design question around healthcare connectivity. Pretty open-ended, which I wasn't fully prepared for.
  • Design a product that helps people connect more effectively with their doctors.

“I went straight to a consumer app idea and spent too long on features before anyone asked me to.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026
Meta DS interview focused entirely on ML evaluation for a recommendation system, specifically logistic regression scoring for restaurant recs. Five questions, all technical, no behavioral fluff. Pretty dense for a single session.
  • How would you design a defensible offline evaluation protocol to compare two recommendation models without access to A/B tests? Walk through the metrics you'd use, how you'd prevent data leakage, how you'd pick K for ranking metrics, and name a pitfall of inverse propensity scoring when propensities get small.
  • Given a holdout set with specific TP, FP, TN, FN counts at a 0.7 threshold, compute precision, recall, specificity, F1, and accuracy. Then explain why accuracy is misleading in this context and which metric best fits the goal of only showing relevant items.
  • How would you check and fix probability calibration in a logistic regression model? Why does calibration matter specifically when you're using a score threshold to make business decisions?
  • Why would you choose logistic regression over a more complex model for this recommendation scoring task? Describe two failure modes of logistic regression and how you'd address them.
  • If your model uses features derived from friend activity, how do you design your train/test split to avoid interference leakage? What are the tradeoffs between time-based, user-disjoint, and graph-clustered splits?

“This was the hardest one.”

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