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Meta interview notes, page 17 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·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Meta for a software engineering role, about five questions covering the usual leadership and conflict territory. Nothing too wild but you need real examples ready, not vague hand-wavy answers.
  • Tell me about a time you took ownership of something that wasn't strictly your responsibility.
  • Describe a situation where you had to lead without formal authority.
  • Walk me through a time you failed and what you did about it.
  • Tell me about a conflict you had with a coworker or teammate and how you resolved it.
  • How have you handled a situation where requirements or priorities were unclear or kept changing?

“I had a decent story here but I rambled on the result part and kind of trailed off.”

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Meta·Machine Learning Engineer·Onsite - Coding / Algorithms

Apr 2026
Meta MLE coding round where they handed me a broken maze-solver and asked me to find and fix two bugs. Pretty focused on reading existing code carefully rather than writing anything from scratch, which I wasn't fully expecting.
  • You're given a maze-solver codebase with two bugs: the start and end markers get overwritten by the path character in the rendered output, and the printed maze doesn't match the expected format. Find and fix both.

“I spent way too long trying to rewrite the rendering logic before realizing the fix was just adding an if/else to give the special markers display priority.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta MLE interview that was basically a compiler/static analysis problem dressed up as a coding question. You parse three-address code, compute time cost from operator weights, and simulate live ranges to get peak memory. More interesting than I expected, but the edge cases section at the end is where things got uncomfortable.
  • Given a file of three-address-code instructions, implement a function that returns both the total time cost (based on operator weights) and the peak memory cost (based on simulated variable live ranges).
  • How would you handle edge cases like constant folding, repeated operands on the same line, and dead code in this instruction parser?
  • How would you extend this solution to support multi-operator expressions if the parser were upgraded to handle them?

“The time cost part was fine, just scan each line and accumulate operator costs.”

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Meta·Software Engineer·Online Assessment (OA)

Apr 2026
Meta coding interview, the classic CodeSignal-style multi-level banking system design question. Four progressively harder stages and you have to get through all of them under time pressure. Not the most creative problem I've seen but the complexity ramps up fast.
  • Design a banking system that supports account creation, deposits, withdrawals, and balance lookups, all processed in timestamp order.
  • Extend the banking system to support a TOP_SPENDERS query that returns accounts ranked by their total outgoing transaction amount.
  • Add TRANSFER (immediate) and SCHEDULE_TRANSFER (executed at a future timestamp) operations, plus the ability to cancel a pending scheduled transfer.
  • Implement MERGE_ACCOUNTS that consolidates one account into another, carrying over the full transaction history and any pending scheduled transfers.

“The first level is straightforward.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Meta with a product design question focused on robotics. Pretty niche angle for a PM loop but not totally surprising given Meta's hardware ambitions.
  • What metrics would you define and track when designing a robot at Facebook/Meta?

“I blanked for a second because I kept thinking about software metrics and had to mentally shift gears.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta data engineer interview with a trie implementation question. Pretty standard coding round, nothing too surprising.
  • Implement a Trie data structure with insert, search, and prefix search functionality.

“I knew the concept but fumbled the implementation a bit.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, one question on string parsing. Pretty straightforward on the surface but there are enough edge cases to trip you up if you're not careful.
  • Given a string, write a function to determine whether it represents a valid number. Valid numbers include integers and decimals like '123', '4.56', or '-78.9'. Strings like '123abc' or '--6' should return false.

“The basic cases are easy but the edge cases pile up fast.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026
Meta SWE coding round, one question about computing mutual friends in a graph. Pretty algorithmic but leaned more on clean implementation and test coverage than raw complexity.
  • Given a User class with an id and a list of friends, implement a function that returns the mutual friends of two users, with no duplicates, and write tests covering edge cases like empty lists and duplicate entries in friend lists.

“The core logic is straightforward, intersection by id, but the test coverage requirement is where they actually spend time with you.”

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

Apr 2026
Meta ML system design round focused entirely on building a weapons detection system for content moderation. Pretty intense scope, they wanted the full picture from labeling to serving to drift monitoring.
  • Design an ML system to detect posts containing weapons (firearms, knives, etc.) on a large-scale social media platform.
  • How would you build the labeling pipeline and handle class imbalance for a weapons detection dataset, including adversarial cases like occluded or low-resolution images?
  • What modeling approach would you use, and how would you handle multimodal signals like image content combined with captions and comments?
  • How would you design the serving layer, specifically around classifying at upload time versus batch re-scanning, and what latency constraints would you factor in?
  • How would you measure success for this system, run experiments to validate changes, and monitor for distribution shift or adversarial drift over time?

“This was the core question and it ate the whole session.”

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Meta·Software Engineer·Hiring Manager Screen

Apr 2026
Meta marketing interview, one question about a campaign that actually worked. Pretty short interaction, not much to report.
  • Can you walk me through a campaign you worked on recently that actually performed well?

“I had an answer ready but second-guessed whether 'recently' meant last quarter or last year.”

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