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Meta interview notes, page 68 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
Meta SWE coding round, one question the whole time, a path simplification problem that sounds straightforward until you factor in relative paths against a working directory context. Felt like a LeetCode medium dressed up with extra edge cases.
  • Given a Unix-style path string (which may be absolute or relative) and a current working directory, return the canonical absolute path. Handle dots, double-dots, consecutive slashes, trailing slashes, and edge cases from resolving relative paths against the given working directory.

“I went straight to stack-based simplification like I always do for path problems, but the relative path piece tripped me up initially.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta SWE onsite coding round with an AI-focused problem. The question was geography-oriented and required implementing an algorithm from scratch, plus writing unit tests to verify correctness.
  • Implement a geography-related AI algorithm that works with map or location data, including debugging and unit test coverage.

“This wasn't a leetcode-style question you can just pattern-match your way through.”

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Meta·Data Scientist·Onsite - Multi Round

May 2026
Meta onsite for a Data Scientist role, this round was heavy on stats and experimentation theory. The interviewer clearly wanted to see whether you actually understand the math behind A/B testing or just know the buzzwords.
  • What's the difference between Type I and Type II errors in A/B testing, how would you decide on acceptable thresholds for each, and if your metric distribution is heavily skewed with outliers, how do you estimate treatment lift and build a confidence interval?

“This is two questions stapled together and the second half is where people fall apart.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

May 2026
Meta SWE coding round, one problem about reconstructing the smallest number from only the odd digits of a given input. Pretty straightforward on the surface but the constraints (up to 1000 digits) mean you can't just treat it as a regular integer.
  • Given a positive integer, extract all its odd digits, sort them in ascending order, and return the resulting number. The input can have up to 1000 digits.

“My first instinct was to just parse it as an int, filter odd digits, sort them, and reconstruct.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a data scientist role. One question, but it was a beast. They basically asked you to walk through a full conflict resolution story with quantitative backing, stakeholder politics, and a hypothetical regression scenario tacked on at the end.
  • Tell me about a time you disagreed with a senior engineer's design proposal when you had no authority to override it. Walk through the stakes, how you built alignment, what trade-offs you proposed, how you handled being challenged in a live review, what the outcome was, and what you'd do if your alternative later caused a regression under unexpected load.

“This question has so many parts that by the time I got to the live design review section I'd already been talking for like four minutes and could feel myself losing the thread.”

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Meta·Software Engineer·Onsite - System Design / Architecture

May 2026
Meta system design round, one question about building a content moderation pipeline for offensive comments. Pretty open-ended and I felt like I was rambling for the first few minutes before finding a direction.
  • Design a system to automatically detect and filter offensive comments at scale.

“I jumped straight into the ML pipeline and the interviewer had to pull me back to ask about ingestion and latency requirements first.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta DS interview with a chatbot metrics and experimentation question. Pretty open-ended, which I wasn't fully ready for. The hints about resolution rate and handle time helped me reconstruct what a solid answer looked like after the fact.
  • For a customer-service chatbot deployed in an e-commerce setting, how would you define the primary success metric? What guardrail metrics would you track, and how would you design an experiment to test whether the chatbot actually improves customer experience?

“I started with CSAT as the primary metric because it felt obvious, but then second-guessed myself mid-answer and pivoted to resolution rate, which I think was the right call.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at Meta for a SWE role. Just one question but it's the kind that trips you up if you haven't actually thought about it before.
  • Tell me about a time you had a significant disagreement with someone and eventually came to realize they were right.

“I fumbled this a bit.”

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

May 2026
Meta ML Engineer interview focused entirely on designing a location-aware recommendation system. The depth they expected was pretty intense, covering everything from geospatial indexing to cold-start to A/B testing in a single session.
  • Design an ML system that recommends nearby places (restaurants, shops, attractions, etc.) to mobile users whose location is continuously updating.
  • How would you handle the training data pipeline, specifically around generating negative samples from impression and click logs?
  • How do you keep the place inventory fresh, and what's your approach to cold-start for newly added venues?
  • What evaluation metrics would you use for this system, and how would you structure an A/B test to validate a ranking model change?

“This was the whole interview basically.”

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Meta·Machine Learning Engineer·Technical Phone Screen

May 2026
Meta ML engineer interview with a classic data structures question that sounds deceptively simple but has some real depth to it if you push past the obvious answer.
  • Design a data structure that supports insert, delete, search, and getRandom, all in O(1) time.

“My first instinct was just 'hashmap' and I said it out loud before thinking it through.”

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