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Meta interview notes, page 170 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
Three coding problems in one Meta session, ranging from interval coverage to BST range queries to a log-parsing stack problem. Nothing felt impossible but the follow-up on the BST question is where I think I left points on the table.
  • Given an integer range [-M, M] and a list of inclusive integer intervals, find any integer in that range with the maximum coverage count across all intervals.
  • Given the root of a BST and two integers low and high, return the sum of all node values in that range. Follow-up: if the tree is huge and you need to handle many different range queries, how would you preprocess it?
  • Given logs of function start and end events with timestamps, compute the exclusive execution time for each function, accounting for nested calls.

“My first instinct was to just expand every interval and count hits in an array, which works but I knew they'd push back on it if M is huge.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Meta system design round for a software engineer role. The question was a full chess platform design with some tricky constraints around undo mechanics and a live leaderboard. Lot of ground to cover in one session.
  • Design an online chess platform where users can challenge friends, undo their last move before the opponent responds, and view a live global leaderboard with ELO rankings at any time.

“This question has a lot of moving parts and I don't think I scoped it well at the start.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Meta product sense interview, one question about designing something for offloading personal belongings. Pretty open-ended and I wasn't totally sure where they wanted me to go with it.
  • Design a product that helps people get rid of personal items they no longer want.

“I went straight to the marketplace angle and kind of regretted it midway through because that space is so crowded.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one problem the whole time: a memory allocator simulation. Felt more like a systems design-lite question dressed up as a coding problem, which I wasn't totally prepared for.
  • You're given a memory bitmap of 0s and 1s. Implement an alloc(x) function that finds the leftmost contiguous run of x free cells starting at an 8-byte aligned index, marks them as occupied, and returns a unique allocation ID. Also implement erase(id) that frees the previously allocated block. Discuss fragmentation behavior under interleaved calls.

“The bitmap part felt manageable at first but the alignment constraint tripped me up.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Meta SWE coding round, one tree problem the whole time. Pretty focused session, they wanted to see clean recursive thinking more than anything else.
  • Given an N-ary tree and two nodes p and q, find their lowest common ancestor. If no common ancestor exists, return null.

“I knew the binary LCA problem cold so I figured this was the same thing.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE interview with a tree problem. Pretty standard stuff but worth sharing for anyone prepping.
  • Given the roots of two binary trees, write a function to determine whether the two trees are identical, meaning they have the same structure and the same node values at every position.

“Classic recursion problem.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta SWE coding round with a two-pointer problem that sounds easy until you actually think about the edge cases. Pretty standard algorithmic interview but the follow-up conditions kept it interesting.
  • Given a sorted integer array and a target value, find the pair of elements whose sum is closest to the target. Return any valid pair if multiple answers exist. Follow-ups included handling a very large or very small target and what you'd do if the array wasn't sorted.

“I went straight for two pointers since the array is sorted, which is the right call.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed at Meta for a software engineer role. The only question I can trace back to this session maps to LeetCode 322 (coin change), a classic DP problem.
  • Given a set of coin denominations and a target amount, find the minimum number of coins needed to make up that amount.

“Classic dynamic programming.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026
Meta data engineer technical screen with two problems back to back, one Python and one SQL. Nothing too wild but the Python one had a wrinkle I didn't fully anticipate going in.
  • Implement a function that takes an arbitrarily nested list of integers and returns a flat list in left-to-right order. Recursion is off the table if nesting can be very deep. Include complexity analysis and tests for edge cases like empty input, deep nesting, and bad element types.
  • Write a SQL query against an events table with user_id, event_time, and event_type columns. Return the count of distinct active users per UTC calendar date. Output should have columns event_date and dau.

“The no-recursion constraint is what tripped me up at first.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Meta for a software engineer role. The problem was building a price-tracking backend, which sounds straightforward until you actually start pulling on the threads around scheduling, deduplication, and storing years of price history cheaply.
  • Design a backend system for an e-commerce price-tracking service that supports millions of users, stores historical price data, and sends alerts when prices drop below a threshold or by a configured percentage.
  • How would you design the dynamic crawl scheduler to fetch prices more frequently for popular products than for long-tail ones?
  • How would you prevent duplicate notifications from being sent after retries or partial failures?
  • How would you store years of price history cost-effectively at scale?
  • How would the design change if you needed to support multiple retailers across different regions?
  • Are you assuming a retailer API, web scraping, or both? What are the tradeoffs?

“I started with the obvious stuff: API layer, a product metadata store, a scheduler that kicks off fetch workers, and a notification service.”

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