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Meta interview notes, page 37 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·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Short Meta interview question about how you'd define success for Facebook Sprouts. Not much context to go on, but it's a product sense question that could catch you off guard if you're not familiar with the product.
  • How would you define success for Facebook Sprouts?

“Tricky because Sprouts isn't a widely publicized product, so if you haven't done your homework you're basically guessing.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta DS interview with a product strategy case around a hypothetical restaurant recommendation feature. One question, but it had a lot of surface area to cover and I don't think I handled the scoping part well.
  • Facebook is considering launching a restaurant recommendation feature. How would you evaluate whether it's worth building, and what data would you look at to make that call?

“I jumped straight into engagement metrics and kind of forgot to size the market first, which probably made my answer feel backwards.”

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Meta·Software Engineer·Take-home Assignment

Apr 2026
Take-home assignment for a software engineer role at Meta, centered entirely on building a RAG tool from scratch using the Mistral API. Pretty involved for a single prompt, covering everything from chunking logic to HTTP serving to retry handling.
  • Build a retrieval-augmented generation tool that can answer questions over a local folder of Markdown and PDF files using the Mistral API, including document ingestion, chunking, and an in-memory vector index.
  • Provide a CLI with commands to index a directory path, ask a question, and serve an HTTP chat endpoint.
  • Implement streaming responses from the chat completions API, include top-k retrieved chunks in the prompt context, and return source citations in the output.
  • Add exponential backoff and retry logic for rate limit errors and timeouts, along with structured error handling throughout.
  • Configure the tool via environment variables for API key, model selection, and port settings.
  • Include a README with setup instructions and minimal tests, then explain your retrieval algorithm choices and describe a quick method to evaluate answer quality.

“The chunking part is where I spent most of my time second-guessing myself.”

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Meta·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Meta SWE onsite coding round. Apparently after mid-April they switched both coding rounds to AI-assisted format, which is a pretty significant change if you're prepping the traditional way.
  • LeetCode problem 339 (referred to by its numeric identifier).

“No details shared about how it went, just that it came up.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Meta ML engineer interview prep tip floating around: walk through 2-3 past projects in a structured way and let the interviewer pick what to dig into. Pretty standard format for technical screens at big tech.
  • Walk me through 2-3 of your most relevant past projects or roles.

“The advice here is to keep each one tight, maybe a minute or two, and cover context, your role, the hardest technical problem, what you did, and what happened.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Meta centered on a product design case for a parking-spot finder integrated with Google Maps. Pretty open-ended and they pushed hard on business model and unintended consequences, which I wasn't fully ready for.
  • Design a parking-spot finder integrated with Google Maps. Walk through the target users and their pain points, the core user journeys, MVP features, go-to-market strategy, and the primary success metrics you'd track.
  • What would the business and revenue model look like for this product?
  • What counter-metrics would you watch to catch unintended consequences from this product?

“I spent way too long on user personas and not enough on the actual product mechanics.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Brutal Meta DS interview centered on a single massive experiment design question. The kind of prompt where you think you understand it and then realize twenty minutes in you've only scratched the surface.
  • You have 1,200 EU businesses in an early-access pool with heavy-tailed chat volumes and expected 20-30% initial subscription take-up. Design a launch test to estimate impact on subscription revenue per business and Resolved-Within-24h Rate. Cover your randomization unit, stratification approach, sample size and power under overdispersion, interference risks from shared customers, ITT vs TOT handling, sequential monitoring, and a pre-registered analysis plan with a decision rule to roll out, iterate, or stop.

“This is basically a full experiment design dissertation compressed into one interview question.”

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Meta·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Product strategy interview at Meta for a leadership role on Facebook Watch. Three questions, all connected, building on each other like a case study. The metrics question felt manageable but the ad format tradeoff at the end is where things got interesting.
  • As the head of Facebook Watch, what metric would you use to measure success?
  • If you needed a second metric to complement the first, what would you pick and why?
  • If you were introducing ads to Facebook Watch, how would you decide between pre-roll and mid-roll ad formats?

“I went with something around watch time retention rather than raw views, since views felt too gameable.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026
Meta data scientist interview with a deep dive into a single metric (comments per daily active user) across six sub-parts covering distributions, bootstrapping, scaling, summary stats, data types, and estimation. The whole thing felt like a stats exam more than a conversation, and some parts I handled better than others.
  • What distribution would you expect for comments per daily active user? Is it discrete or continuous, and why might Poisson be a poor fit?
  • You resample 10,000 users with replacement 100,000 times and compute the sample mean each time. What does that bootstrap distribution look like, and what determines whether it's approximately normal?
  • If you double the sample size from 10,000 to 20,000, how does the width of the bootstrap distribution of the mean change, and why?
  • Compare mean, median, mode, and p95 for this metric. Which is most stable, which matters most for decisions, and how do you handle p95 for a discrete count variable?
  • The per-user count is an integer but the mean is a real number. What can go wrong when storing or aggregating these at scale, and how do you keep things numerically stable?
  • Given that per-user variance exceeds the mean (overdispersion), write out the standard error of the sample mean and explain when you'd use trimmed means, Winsorization, or a covariate-based variance reduction approach.

“This part I actually felt okay about.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Round 5 at Meta for a software engineer role, and they threw an AI system design question at me about building an agent to manage failures in a large-scale pipeline. Pretty deep into the process at that point so the stakes felt real.
  • Design an AI agent that can detect and handle issues in a large-scale data or processing pipeline.

“This one had a lot of surface area and I spent probably too long on the detection side before even touching remediation.”

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