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Meta interview notes, page 89 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·Onsite - Coding / Algorithms

May 2026
Meta SWE coding round with a string manipulation problem. Pretty standard backtracking territory but the constraint around unique characters across concatenations is easy to fumble if you're not careful with how you track state.
  • Given an array of strings, select and concatenate any subset in any order such that the resulting string has all unique characters. Return the maximum possible length of such a string.

“The example they give is almost too clean, 'un' + 'iq' = 'uniq', length 4.”

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Meta·Data Scientist·Technical Phone Screen

May 2026
Meta data scientist interview with a heavy stats and experimentation focus. The question was a multi-part monster about estimating central tendency on zero-inflated comment count data, with bootstrapped CIs and effect size reporting on top. Walked out not totally sure how I did.
  • A website's per-user daily comment counts are heavily skewed and zero-inflated. You're evaluating a backend change meant to boost engagement. When would you prefer mean, median, 10% trimmed mean, winsorized mean, or geometric mean of (1+count) as your central tendency estimator? Walk through the bias/variance trade-offs under heavy tails and what each means for product decisions.
  • Control group counts are [0,0,0,1,1,2,2,3,20,50] and treatment counts are [0,0,1,1,1,2,2,3,5,10]. Compute mean, median, 10% trimmed mean, and winsorized mean for each group. Which estimator most reliably detects a real improvement here, and why?
  • How would you construct a 95% confidence interval for your chosen estimator using a nonparametric bootstrap, stratified by user activity buckets? What assumptions does this require and how would you verify them?
  • If you need to report an effect size that's robust and comparable across different experiments, what transformation and effect metric would you propose? For example, a log1p-based percent change or a quantile treatment effect at a specific quantile. Defend your choice.

“This part I actually felt decent about.”

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

May 2026
Meta ML engineer interview focused entirely on designing an ads ranking system from scratch. Pretty deep dive covering everything from problem framing to serving infrastructure, and calibration came up in a way I wasn't fully prepared for.
  • How would you frame the core ML problem for a sponsored ads ranking system? What are you actually predicting and how does that feed into the final ranking?
  • Walk me through the feature engineering you'd do for an ads ranking model. What feature groups matter most and how do you handle interactions between them?
  • What model architectures would you consider for ads ranking and what are the tradeoffs between them?
  • Why does probability calibration matter in an ads auction, and how would you actually calibrate your model's outputs?
  • Describe the training and serving infrastructure for this ads ranking system at a high level, including how you'd validate changes before full rollout.

“I went straight to CTR prediction, which was fine, but the follow-up pushed me on whether CTR alone is enough for ranking.”

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Meta·Product Manager·Onsite - Behavioral / Leadership

May 2026
Meta PM interview, one question that sounds deceptively simple but really isn't. The 'non-obvious insight' framing is doing a lot of work and I didn't fully appreciate that until I was already mid-answer.
  • Tell me about a non-obvious insight you had that drove meaningful business impact.

“The word 'non-obvious' is carrying all the weight here and I kind of glossed over it.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
Meta PM interview with a single product design question about theme parks. Pretty open-ended, which I wasn't fully ready for.
  • Design a product for theme parks.

“I went straight to the user segments and started listing out pain points, probably too fast.”

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Meta·Software Engineer·Technical Phone Screen

May 2026
Meta SWE coding round with a bitmask subset problem. Pretty clean problem once you see the trick, but I spent way too long second-guessing the preprocessing step.
  • Given an array of strings, find the maximum length of a concatenated string formed by choosing a subset such that every character in the result appears exactly once.

“The bitmask approach clicked for me pretty fast since each string maps cleanly to a 26-bit integer.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Meta focused on the newsfeed product. Just the one question from what I can tell, but it was meaty enough to fill the session.
  • As the PM responsible for the newsfeed, how would you decide whether to increase the proportion of videos versus events shown to users?

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

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Meta·Software Engineer·Online Assessment (OA)

May 2026
Meta SWE coding question about computing a maximum score from a list of book scores. Pretty straightforward algorithmic problem but the edge cases are where things get interesting.
  • Given a list of integers representing book scores, write a function that returns the maximum possible score a student can achieve from those books.

“Looked at the examples and the first thing I noticed is that [5, 3, 8, 6] gives 22, which is just the sum of all elements.”

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Meta·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral loop at Meta for a Data Scientist role, focused heavily on ownership and going beyond your job description. The question was layered and they really wanted specifics, not vibes.
  • Tell me about a time you took on work outside your official scope to drive a measurable business outcome. Walk me through the context, what you did, how you got alignment, what risks you took on, the actual impact, any pushback you faced, and something you'd do differently looking back.

“This is a beast of a question because it's seven questions stitched together and the interviewer will absolutely notice if you skip one of the parts.”

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Meta·Software Engineer·Recruiter / HR Screen

May 2026
Short HR phone screen for a Product Design Engineer role at Meta. Two questions, both pretty standard fit stuff, but they wanted specifics not just enthusiasm.
  • Why do you want to move into the Consumer Electronics space, and why this team specifically?
  • Why are you a good fit for this role?

“I had an answer but it felt thin in the moment.”

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