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Meta interview notes, page 151 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Meta MLE interview focused heavily on a single ML system design problem around feature engineering for the News Feed ranking model. The follow-up about offline vs online metric gaps was where things got interesting and honestly where I felt most out of my depth.
  • An existing model predicts the probability a user likes a News Feed post. How would you add a feature tracking whether a user liked posts from a given author (or a specific post) in the last 6 months? Walk through feature engineering, how you'd serve it at inference time, and how you'd avoid label leakage during training.
  • How would you integrate this new feature into the existing model? Should it go in as a raw value, an embedding, or some kind of interaction term with existing features?
  • How would you validate whether this feature actually helps? What offline metrics would you look at and how would you slice the analysis?
  • Suppose your offline metrics improve significantly but the online A/B test shows no meaningful lift. What could explain that gap and how would you debug it?

“This is where I spent most of the interview.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineer role, single question about building a news aggregation service on top of RSS feeds. The question had a lot of surface area and I felt like I was racing the clock the entire time.
  • Design a system that pulls news items from RSS feeds across many companies and lets users query the top X most recent articles per company.

“I started with requirements which felt right, but I spent way too long on the ingestion side and barely got to the serving path before they nudged me along.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta coding round for a software engineer role. The whole session was basically one meaty algorithmic problem that kept getting harder as the conversation went on. Walked away feeling okay about the non-negative case but a bit shaky on the follow-up.
  • Given an integer array and an integer k, find all subarrays whose length is a power of two and whose sum falls in the inclusive range [k, 2k]. Return the index ranges, and your solution must be faster than O(n^2).
  • How would you adapt your approach if the array can contain negative values? Propose a strategy using prefix sums combined with either a balanced tree or hash-bucketing, and analyze the time complexity.

“The power-of-two length constraint is what makes this interesting.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round focused entirely on one meaty problem about tracking top customers by activity in near real time. The question had a lot of moving parts and I don't think I got through all of them cleanly.
  • Design a system that tracks the top N customers by cumulative activity (deposits, payments, and successful transfers only) in near real time, with efficient updates and queries. Cover data structures, API design, update/query complexity, late accept events, expired transfers, tie-breaking logic, dynamic N, and scaling to millions of customers.

“This one spiraled fast.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, got a classic shuffling problem. Nothing too surprising but the O(1) space constraint is the part that trips people up if they haven't seen it before.
  • Given n items labeled 0 through n-1, generate a uniformly random permutation in O(n) time using a standard random integer function. Extra space should be minimized.

“I knew Fisher-Yates but blanked for a second on why you swap in-place instead of building a separate structure.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round with a tree construction problem. Not a lot of context given about the interview itself, but the problem was interesting enough to write about.
  • Given a list of up to 1000 digits, build an N-ary tree from them where each node can have any number of children, then return the level-order traversal of that tree.

“The tricky part is that the problem doesn't tell you how many children each node gets, you have to decide.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineer role, focused entirely on building an ads impression aggregation service. The question had a lot of moving parts and the follow-up on write throughput pushed into territory I wasn't fully prepared for.
  • Design an ads impression aggregator service that ingests high-volume impression events and serves per-ad hourly counts via an API.
  • How do you handle duplicate and out-of-order impression events in the ingestion pipeline?
  • How would you handle very high write throughput to the database from the stream processor?

“I started with the ingestion pipeline since that felt most concrete: ad servers pushing into Kafka, a stream processor doing windowed aggregation by ad and hour, then writing into something like Cassandra keyed on that pair.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta coding screen with a digit-counting problem that looks easy but has a few edge cases worth thinking through carefully.
  • Given two integers left and right (both three-digit numbers, 100 to 999), count how many integers in the range [left, right] have all three digits distinct from each other.

“Looks trivial at first.”

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

Jun 2026
ML system design round at Meta for an MLE role. The whole session was focused on building a content moderation pipeline to catch policy-violating ads, which sounds scoped but actually sprawls in a lot of directions fast.
  • Design a system to detect ads containing weapons or other policy-violating content, covering multimodal inputs like images, text, and video.
  • How would you approach labeling and establishing ground truth for this kind of policy violation detection task?
  • Walk me through how you'd design the model itself, including how you'd combine signals from different modalities.
  • How would you handle online inference given latency and throughput constraints for a high-volume ads platform?
  • How do you decide on classification thresholds, and how do you think about the precision vs recall tradeoff in a content moderation context?
  • How would you design the human review queue that sits downstream of the classifier?
  • How would you make the system robust against creators who try to evade the filter, for example by obfuscating images or using coded language?

“This is the core question and it's deceptively wide.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Interviewed at Meta for a product role and got hit with a metrics question that sounds deceptively straightforward until you actually try to answer it.
  • How would you define metrics to measure meaningful social interactions?

“I went straight to engagement numbers and immediately felt the interviewer cooling off.”

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