Meta interview notes, page 27 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·Data Scientist·Technical Phone Screen

Apr 2026

Meta DS interview, product analytics focus. The whole thing was one long case about a ranking change for a multi-account product, and they wanted both a historical analysis angle and a full experiment design. Dense question, not a lot of warmup.

A multi-account product currently ranks accounts by most recent visit. The team wants to switch to ranking by highest notification count instead, with recency as a tiebreaker. Using historical session-level logs, how would you assess whether this change is likely to be beneficial before running an experiment?
Design an A/B test to measure the causal impact of the new notification-based account ranking. Walk through the eligible population, randomization unit, primary and secondary metrics, guardrails, power and MDE considerations, and any key segments or edge cases.

“I started talking about correlational analysis between notification rank position and account selection rate, which felt right, but I kept second-guessing myself on the selection bias piece.”

View Post

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

Apr 2026

Went through a design round at Meta for an MLE role and came away a bit frustrated. The 45-minute format sounds fine on paper but once the interviewer is late and you do intros, you're really working with maybe 30 minutes. Both rounds were supposedly AI-enabled but that turned out to be basically meaningless in practice.

Design an event recommendations system, including specific model architecture and implementation details.

“The depth they wanted here was kind of brutal given the time crunch.”

View Post

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

Apr 2026

Meta ML engineer interview with a meaty system design question about building an evaluation framework for ads ranking. Not a lot of context given upfront, so you have to drive the structure yourself.

Design an evaluation framework for ads ranking.

“This one is broader than it sounds.”

View Post

Meta·Data Scientist·Technical Phone Screen

Apr 2026

Meta DS interview with a VR analytics scenario, three questions rolled into one prompt covering SQL aggregation, category breakdowns, and a hypothesis test. Felt more like a take-home vibe but it was live. The stats piece at the end is where things got interesting.

Given VR usage logs joined with an app catalog, which app had the highest total usage duration over the last 30 days?
What percentage of total VR session time in that 30-day window belongs to each app category?
Test the hypothesis that users of social apps are more engaged than users of game apps. Define an engagement metric, outline the SQL to compute it, and describe the statistical test you'd use.

“Pretty standard aggregation question.”

View Post

Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta coding screen, pretty much just the one problem the whole time. Nothing fancy about the setup but the question had more edge cases than I expected going in.

Given an array of integers and a value k, find all pairs of elements whose difference equals k.

“Started with the brute force two-pointer approach and the interviewer just kind of waited.”

View Post

Meta·Data Scientist·Technical Phone Screen

Apr 2026

A Meta DS interview focused almost entirely on one big ads ranking question. It was a deep system design meets experimentation hybrid and I was not fully prepared for how much ground it expected you to cover.

You work at a social network with an auction-based ads marketplace. A new ads ranking algorithm has been proposed. Walk through how you would evaluate it and decide whether to roll it out, covering offline evaluation, online experiment design, metrics, result interpretation, and your final recommendation.

“This is basically a full case study crammed into one question.”

View Post

Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta SWE coding round, one question on tree traversal. Pretty standard stuff but the complexity analysis at the end is where they actually pay attention.

Given a binary tree where each node holds a single digit, every root-to-leaf path spells out a number by concatenating the digits top to bottom. Return the sum of all such numbers across the entire tree.

“I knew DFS was the move pretty quickly, carry a running number down each path and when you hit a leaf you add it to the total.”

View Post

Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

PM interview at Meta's Menlo Park office, one product strategy question about internal operations and tradeoffs. Pretty niche scenario but actually made me think harder than the usual metrics questions.

Meta's Menlo Park office needs to cut either a fixed bus route or an on-demand rideshare program. What metrics would you use to make that call, and what are the tradeoffs?

“I spent the first minute just trying to figure out what they actually cared about.”

View Post

Meta·Data Scientist·Technical Phone Screen

Apr 2026

Meta DS interview focused almost entirely on experiment design, and it went deep fast. The A/B testing questions were layered and connected, not just isolated prompts, so you had to keep your earlier answers consistent as new constraints got added.

Given a baseline conversion rate of 4%, a 10% relative uplift to detect, two-sided alpha of 0.05, and 80% power, compute the minimum per-variant sample size. Show the formula and the numeric result.
You now have two primary metrics: conversion rate and qualified-lead rate. Apply a Bonferroni correction to the sample size calculation, then explain the trade-offs of that approach versus using a gatekeeper metric or a hierarchical testing structure.
With 1.2M eligible sessions per day, 70% passing eligibility checks, and a cap of 20% of eligible traffic for the experiment, estimate how many days the test needs to run. Then propose a ramp schedule that keeps the test statistically valid while managing operational risk.
When would you use a z-test versus a t-test for this kind of experiment, and if your 95% confidence interval barely excludes zero, what does that actually tell you?
Before looking at experiment outcomes, how would you verify that randomization worked correctly and that treatment and control groups are balanced across dimensions like country and platform?

“I knew the formula but fumbled the z-values from memory mid-call.”

View Post

Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Interviewed at Meta for a machine learning or data-related role, got asked a pretty foundational question about prediction serving. Short and to the point.

What is the difference between batch predictions and real-time predictions?

“Seems basic but I fumbled the tradeoffs a bit.”

View Post