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Meta interview notes, page 228 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

Jul 2026
Meta SWE interview with a log sorting problem. Pretty straightforward premise but the follow-up about inconsistent formats is where things got interesting.
  • Given a list of logs with timestamps, sort them according to some business logic. How would you handle inconsistent log formats to make sorting work?

“The base sorting part was fine, I talked through a custom comparator and parsing the timestamp out of each string.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Went through a technical phone screen at Meta for a software engineering role. Two questions, one graph problem and one string math problem. Nothing too wild but the multiply strings one had a subtle carry edge case I wasn't fully prepared for.
  • You have N distinct islands and a list of undirected paths connecting some of them. What is the minimum number of additional paths needed to make all islands reachable from each other?
  • Given two non-negative integers represented as strings, return their product also as a string, without converting directly to integers.

“Classic connected components problem.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE coding round with an ads ranking problem. Pretty domain-specific for a coding question, felt more like a product-adjacent algo problem than pure leetcode.
  • Given a list of ads with CTR, Bid, and Expected Clicks metrics, implement a function that returns the ad IDs sorted by rank. Sort primarily by CTR descending, then Bid descending as a tiebreaker, then Expected Clicks descending if bids also tie.

“The multi-key sort itself isn't hard, Python's tuple sort makes it pretty clean.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta coding screen with two problems back to back. The first was pretty standard but the second had some nuance that I didn't fully appreciate until I was already mid-solution.
  • Given an integer array and an integer k, return the k-th largest element (0-indexed, so k=0 is the maximum).
  • Given a calendar represented as an array of 'H' (holiday) and 'W' (workday) characters, and a number of PTO days you can use, find the maximum length of a consecutive vacation streak you can create.

“Went with a sort and index approach first, then they asked about optimizing it.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta data scientist interview, SQL-heavy with a focus on ads revenue analysis. The question looked straightforward at first but the follow-ups about pitfalls and metric improvements were where things got real.
  • Write a SQL query to calculate Year-over-Year ads revenue by month. What are the pitfalls of using a rolling sum in this context, and how would you improve or complement the YoY growth-rate metric?

“The SQL part was fine, LAG() over a partition by month got me there.”

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Meta·Data Scientist·Technical Phone Screen

Jul 2026
Meta DS interview focused on product analytics for Oculus. One SQL question with a conceptual follow-up about metric definitions. Pretty straightforward but the open-ended 'which metric is best' part is where I think they actually care about your answer.
  • Write a SQL query to find the most-used app over the last 7 days relative to a given date. Return the app ID, app name, and your chosen usage metric.
  • What are the trade-offs between different definitions of 'most used app': number of sessions, total time spent, and unique users?

“I went with session count as my metric since it felt cleaner to defend than total duration.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE coding round, two problems back to back. Both were pretty standard but the sparse vector one had a follow-up direction I didn't fully anticipate.
  • Given two sparse vectors of the same length, compute their dot product efficiently using a sparse representation like a list of (index, value) pairs.
  • Count the number of islands in a 2D grid where '1' is land and '0' is water, with 4-directional connectivity.

“My first instinct was to just use a hashmap for one vector and iterate through the other.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for an MLE role at Meta, entirely focused on research experience. The whole thing was basically: pick a project you owned, then defend every decision you made for 45 minutes.
  • Walk me through a research project you personally led, including the problem, your hypothesis, and how you ran experiments.
  • What happened when your results didn't match your hypothesis, or when an experiment failed to show what you expected?
  • Describe a time you disagreed with a collaborator or stakeholder about the direction of a project. How did you handle it?
  • How did you decide what to prioritize when you had multiple competing research directions or limited resources?
  • What was the actual impact of the research you described? How did you measure it or communicate it to others?

“This is the core question and they will stay here for a long time.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE interview with a systems-thinking coding question. The problem was more about wiring together a loop correctly than any clever algorithm, which I didn't fully appreciate until I was mid-explanation.
  • Implement a ReAct-style agent execution loop given three APIs: one that calls a language model, one that parses its output, and one that executes a tool. The loop should run up to a max number of steps, accumulate context in a prompt, and terminate early if a final answer is found.

“I kept wanting to jump into edge cases before I even had the happy path written, which burned time.”

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Meta·Software Engineer·Technical Phone Screen

Jul 2026
Meta SWE interview that dropped a buggy React codebase in front of me and said 'find the problems.' Not the abstract whiteboard stuff I'd been drilling for, so that was a bit of an adjustment.
  • You're given a React codebase with multiple bugs and performance issues. Walk through how you'd identify unnecessary re-renders, event-binding leaks, and misaligned rendering logic. As a follow-up: what's your approach to fixing a component that's noticeably slow?

“The re-renders part I handled okay.”

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