Meta Interview Questions — Page 175

Meta·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Meta data engineer interview, system design round focused entirely on dimensional modeling. Five sub-questions stacked into one long prompt, which felt like a lot to juggle in real time.

Design a dimensional model for a transactional analytics use case. Define the core fact table including its grain, keys, and measures, and outline four or five dimension tables.
If you need to track how dimension attributes change over time, how would you implement SCD Type 2?
Describe the relationship between two example tables, specifically contrasting one-to-many and many-to-many cardinalities, and explain how each affects your schema design.
How would you handle a many-to-many relationship in your schema, for example using a bridge table or an additional fact table, and what are the trade-offs between those approaches?
How would you evolve your schema to accommodate new requirements such as adding a column, introducing a new dimension, normalizing a table, or adding a new fact, while keeping a single authoritative fact table and maintaining scalability?

“Started okay, picked an e-commerce transaction as my example and defined grain at the order-line level.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Meta PM interview focused on the ads product area, two questions back to back with no fluff. Pretty classic Meta product sense territory but the GenAI angle on the second one made it feel more current.

You're the PM for Facebook Ads. How would you define success metrics for the team?
You're thinking about adding GenAI features to the ad creation flow for new sellers. How do you decide whether to ship it?

“I went straight for advertiser ROI and kind of ignored the platform health angle for too long.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for an EM role at Meta, got a behavioral question about change management. Pretty standard stuff but it made me realize I hadn't thought through how I actually do this versus how I think I do it.

How would you communicate a major strategic shift from leadership down to your team?

“I rambled a bit.”

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Meta·Software Engineer·Technical Phone Screen

Jun 2026

Meta mobile engineer interview with an algorithms question about finding a local minimum in an array. Not much else to go on from what was shared, but it felt like a straightforward technical screen.

Given an array of integers, find a local minimum, meaning an element that is smaller than its neighbors.

“Classic binary search variant but my first instinct was just linear scan, which works but they clearly wanted the O(log n) approach.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

A deep-dive stats question at Meta for a Data Scientist role. The whole interview was basically one massive multi-part problem about modeling skewed count data, and it went places I did not fully expect.

Daily comment counts per post on a large social app are highly skewed with many zeros. Which discrete distribution would you choose among Poisson, Negative Binomial, Poisson-lognormal, and discrete power-law, and why? Justify based on overdispersion, tail behavior, and zero inflation.
Walk through a step-by-step model selection plan for this count data problem, covering dispersion tests, MLE fitting, likelihood-ratio tests, AIC/BIC comparisons, Vuong's test, power-law tail estimation via KS minimization, and visual diagnostics like QQ-plots.
Using your chosen model, how would you estimate P(Y <= 1) and the 95th percentile of comments per post, including bootstrap 95% confidence intervals? How do you handle standard errors and small-sample bias?
How would left-truncation of zero-comment posts, right-censoring from late-arriving comments, and mixing of bot versus human behavior bias your model estimates, and what corrections would you apply?
If threaded replies launch next month, how would you predict the qualitative change in your model parameters, and how would you revalidate model fit one week after launch?

“This is where I spent most of my time and honestly fumbled the ordering of my reasoning.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round at Meta for a software engineering role. Three prompts, all pretty standard but the last two caught me more off guard than I expected.

How do you align requirements when working across multiple teams?
What is the biggest mistake you have made at work?
Tell me about a time you dropped the ball on something you were supposed to deliver.

“I talked about syncing early with stakeholders and setting shared definitions of done before any real work starts.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

Meta DS interview focused entirely on an ads ranking experiment scenario. Six questions, all interconnected, building from metric selection through exec communication. The depth expected at each step was pretty significant for a single session.

What primary and guardrail metrics would you track when launching a new ad recommendation system to replace a rule-based ranker, and why those specifically?
How would you design the A/B test to ensure control and treatment groups are truly comparable when rolling out this system?
Write out the formulas for estimating required sample size and test duration for this experiment.
If treatment CTR goes up 5%, does that mean advertisers will necessarily spend more? Walk through the causal path and any additional analyses you'd run.
You're shown a chart where the treatment group already has higher CTR than control before the experiment even launches. What's wrong and how would you fix the visualization?
How would you summarize the experiment results, next steps, and risks for a senior leadership audience?

“I went straight for CTR as primary and ad spend as guardrail, which felt right but I didn't do a great job explaining the hierarchy.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for a PM role at Meta and got hit with a behavioral question about leadership under pressure. Pretty standard stuff but it still tripped me up a bit.

Tell me about a time you had to step up and lead through a difficult situation.

“I had a story ready but it felt thin the moment I started telling it.”

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Meta·Data Scientist·Technical Phone Screen

Jun 2026

Meta DS interview with a product analytics case about estimating fake accounts on the platform. Pretty open-ended, which I wasn't fully prepared for.

How would you design an analytics approach to estimate the number or proportion of fake accounts on a large social platform? Walk through your data signals, sampling strategy, and how you'd validate the estimate.

“I started with supervised classification which felt natural but the interviewer kept pushing on how you'd even get ground truth labels at scale.”

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Meta·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026

Went through a coding round for an MLE role at Meta. Three questions, all algorithmic, nothing ML-specific which surprised me a bit. Pretty standard faang-style session but the median of two sorted arrays one nearly broke me.

Given an m x n binary grid of 0s and 1s, find the size of the largest connected component of 1s using 4-directional connectivity.
Given two separately sorted arrays, find the median of all elements combined without merging the arrays first. How do you do it optimally and what's the complexity?
Implement BFS and DFS over a potentially disconnected undirected graph, returning the full visitation order across all vertices. Cover both recursive and iterative versions and discuss complexity.

“This was the warmup and I treated it like one, which was fine.”

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