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Netflix interview notes, page 15 of 20

FAANG / Big Tech

Most of these Netflix write-ups come from Software Engineers (136), then Data Scientists (23) and Product Managers (16). The most reported rounds are the technical phone screen (94), the system design round (48), the product sense round (14) and the behavioral / leadership round (11).

191 interview notes · updated Jul 2026

Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data scientist interview focused on causal inference, specifically a scenario around propensity score matching for a UI experiment. Heavy on methodology and the kind of stuff you only know cold if you've actually run observational studies before.
  • Why is a standardized mean difference of 0.1 or below commonly used as the balance threshold in propensity score matching?
  • If logistic regression isn't the right fit for the propensity score model, what alternatives would you use and why?
  • After matching, how would you diagnose whether residual confounding is still a problem?
  • Walk through one method for estimating the variance of the treatment effect under propensity score matching.

“I knew the answer but fumbled the explanation a bit.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Interviewed at Netflix, got hit with a stats question that I was not fully prepared for. Short and focused, but it stuck with me.
  • What do you do when your test metric is not normally distributed?

“I fumbled around with 'use a non-parametric test' and mentioned the Mann-Whitney U, which was fine but felt incomplete.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Netflix system design round, one big open-ended question about building a crash-resilient file system. Felt like a grad-school OS exam more than a typical interview. Walked out not totally sure if I nailed it or completely missed the point.
  • Design a crash-resilient file system (in-memory or disk-backed) that supports create, read, write, and delete operations, snapshots, and restore from snapshot. Walk through your data model, how you'd handle durability, the recovery flow after a crash, and the main bottlenecks. Then discuss what changes for production scale.

“I started with the data model because I needed something to anchor the whole thing.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Netflix PM interview, one question about diagnosing a metric drop on a live video feature. Pretty standard product analytics case but the IG Live framing threw me off a bit since this was for Netflix.
  • You're a PM on the live video team and viewership has been declining steadily by 10%. What's causing it and what do you do about it?

“I started with segmentation, which was the right call, but I spent too long on the diagnosis side and barely got to the 'what do you do' part before time ran out.”

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Netflix·Software Engineer·Technical Phone Screen

Jun 2026
Netflix coding screen for a software engineer role. One problem, sliding window flavor, seemed clean on the surface but the details tripped me up a bit.
  • Given a user's watch history as a list of (episode_id, day) pairs and an integer K, determine whether the user watched the same episode more than once within any K-day sliding window. Return True if a re-watch within K days exists, otherwise False.

“My first instinct was a brute force double loop and I almost said it out loud before catching myself.”

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Netflix·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Netflix SWE interview that leaned heavily on their culture principles and how you've actually lived them, not just recited them. The memo-based format was a bit of a curveball if you weren't expecting it.
  • Walk me through a memo you prepared for a project or opportunity. What were the goals, key decisions, trade-offs, metrics, and expected impact?
  • Which parts of your memo connect to our company's culture principles, and where have you demonstrated those principles in previous roles?
  • Give two specific examples from your past work that show those cultural behaviors and the results they produced.
  • What seniority level do you think you're operating at, and why? Back it up with scope, ownership, and measurable results.

“This one tripped me up a bit because I hadn't prepped a literal memo, so I had to reconstruct one on the fly from a past project.”

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Netflix·Software Engineer·Technical Phone Screen

Jun 2026
Netflix software engineering interview with three back-to-back coding problems. Nothing too wild algorithmically but the follow-up design questions on the cache one caught me a bit off guard. Overall felt like a solid mid-level coding screen.
  • Implement an in-memory key-value cache where each entry has a time-to-live. Include put and get operations, and design a background cleanup process for expired entries.
  • Build an undo manager that executes commands and supports undoing the most recent one. Define what happens when there's nothing to undo, and extend it to support redo.
  • Given an organization hierarchy as a rooted tree where each node has an id and children, implement a depth-first traversal returning node ids in DFS order.

“Started fine with a plain hashmap storing value plus expiration timestamp.”

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Netflix·Software Engineer·Recruiter / HR Screen

Jun 2026
Standard Netflix HR screen, basically a background check with a culture memo twist. They really do quiz you on the memo, so read it before you get on the call.
  • Why do you want to join Netflix?

“You can't just say you like the product.”

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Netflix·Software Engineer·Technical Phone Screen

Jun 2026
Netflix coding screen, one question about arrays and string runs. Pretty short session, felt more like a warmup than a full round.
  • Given an array of strings, find the longest subarray where all elements are the same consecutive value.

“Seemed straightforward at first.”

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Netflix·Software Engineer·Technical Phone Screen

Jun 2026
Netflix coding screen focused on a deduplication problem tied to their actual product UI. Pretty applied for a phone screen, felt less like a generic leetcode grind and more like something a Netflix engineer would actually think about.
  • Given a list of shelves where each shelf contains titles, and a viewport width X, deduplicate titles across all shelves so that no title appears more than once in the visible portion of any shelf. Return the resulting shelves.

“The product framing helped me actually think straight.”

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