Meta Interview Questions — Page 139

Meta·Data Scientist·Technical Phone Screen

Jun 2026

Got a deep ML system design question for a Data Scientist role at Meta. One question, but it had like six sub-parts packed into it, so it basically ran the whole session. Dense and a bit exhausting, but also kind of interesting if you're into autonomous systems stuff.

You have trajectory data (position, velocity, maybe acceleration) for multiple vehicles at an intersection over the last several seconds. Build an ML system to predict each vehicle's intention: left turn, straight, or right turn. Walk through labeling strategy, feature engineering vs sequence models, multi-agent interactions, evaluation metrics, class imbalance, distribution shift, and how you'd use uncertainty estimates in downstream planning.

“This question is basically six questions stapled together.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Meta SWE coding round with a geometry/array problem that looks deceptively clean until you actually try to optimize it. The O(n^2) path is obvious but they clearly wanted more.

You're given an array of mountain peak heights and a minimum index gap constraint. Find the pair of indices at least that gap apart where the absolute difference in heights is smallest, and return that minimum difference.

“Started with brute force just to make sure I understood the problem, which was fine.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026

Meta SWE interview that leaned hard into algorithm depth. One question, two approaches, plus a stream variant tacked on at the end. More to unpack than it looks.

Given an unsorted array of n integers and a value k, return the k-th largest element without sorting the whole array. Walk through two approaches: a randomized linear-time selection algorithm and a heap-based method. For each, cover the algorithm itself, time and space complexity, and how you'd extend it to handle a data stream instead of a static array.

“This felt like three questions stitched together.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Meta system design round focused entirely on building an online judge from scratch. One big open-ended question for the whole session, which sounds manageable until you realize how many subsystems are hiding inside it.

Design an online code judging platform where users submit code for a problem and receive a verdict such as Accepted, Wrong Answer, Time Limit Exceeded, Memory Limit Exceeded, Runtime Error, or Compilation Error. Cover problem and test case storage, a submission queue, sandboxed execution workers with resource limits, language runtime management, the full judging pipeline from compile to verdict aggregation, result storage, submission history, leaderboard, anti-abuse mechanisms, and fair resource sharing across users. Also discuss scaling the worker pool, isolating untrusted code, and handling infinite loops or runaway submissions.

“I started with the happy path, compile then run then return a verdict, and felt pretty good for the first few minutes.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026

Product analyst interview at Meta with a cross-functional product sense question framed around Google Ads. Single question, felt more like a metrics framework exercise than anything deeply product-specific.

You're a PM for Google Ads. What metrics would you use to evaluate whether the product is succeeding?

“I went straight to advertiser ROI and click-through rates, which felt right but probably too surface-level.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Meta MLE interview with a probability/sampling problem that looks deceptively simple on the surface. The trick is knowing the right data structure approach before you even start coding.

Given a 0-indexed array of positive integers where each value represents a weight, implement a function that randomly picks an index such that the probability of selecting index i equals w[i] divided by the total sum of all weights.

“My first instinct was to just expand the array and pick uniformly, which works but is terrible on memory for large weights.”

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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 a Ticketmaster-style ticketing platform. Pretty deep scope for a single question and I felt like I was playing catch-up for most of it.

Design a large-scale ticketing system similar to Ticketmaster, scoped to general admission events. Walk through the data model, purchase flow, inventory holds with expiry, oversell prevention, payment integration, idempotency, surge handling at on-sale time, and refunds.

“This question is basically five questions duct-taped together and they want you to cover all of it.”

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Meta·Backend Engineer·Technical Phone Screen

Jun 2026

Meta backend interview with a tree problem that looks manageable until you actually try to implement it cleanly. The boundary traversal question has a lot of edge cases that will eat you alive if you're not careful going in.

Given the root of a binary tree, return the values along its boundary in anti-clockwise order starting from the root. The boundary includes the left boundary (excluding leaves), all leaves left to right, and the right boundary in reverse (excluding leaves). Handle edge cases like single-node trees and trees with only one subtree.

“I thought I had this.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Meta for a SWE role, focused entirely on building a ticketing platform like Ticketmaster. The interviewer pivoted mid-session to a much smaller scope, which threw me off more than I expected.

Design a ticket purchasing system that handles seat inventory, seat holds, and flash-sale concurrency at scale.
How would you redesign this as a single-machine ticketing system for a small school, with no distributed infrastructure?
How do you keep the event browsing and search experience fast during a high-traffic on-sale event?
Walk through the seat reservation state machine from hold to confirmed or released.

“I started with the distributed locking angle pretty quickly, maybe too quickly.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

Technical screen for a Data Scientist role at Meta, focused entirely on a deep open-ended problem around autonomous driving simulation realism. One question, but it sprawled into five or six sub-problems and easily ate the full hour.

You have real-world driving logs and simulator outputs for the same set of scenarios. Design an end-to-end approach to evaluate whether the simulator is realistic enough to use for vehicle performance evaluation. Cover what 'realistic' means operationally, which metrics you'd compute across trajectory, interaction, and safety levels, how you'd structure the real-vs-sim comparison, how you'd validate that simulator improvements actually translate to better real-world correlation, and what failure modes you'd watch for.

“This question is enormous and I think I underestimated that at first.”

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