Netflix interview notes, page 9 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·Software Engineer·Onsite - System Design / Architecture

May 2026

Netflix system design round focused on a deduplication problem for the homepage feed. The coding portion was manageable but the production scaling discussion is where things got real fast.

Implement a deduplication function for a 2D list representing a homepage feed (rows of horizontally scrolling content). The function should remove duplicate show names using both a global visited set across the whole page and a per-row local visited set, scanning top-to-bottom and left-to-right.
How would you scale this deduplication system for production at Netflix scale? Consider things like Redis Bloom Filters, per-user vs global dedup, TTL on the visited set, and integration with the ranking layer.

“The two-level dedup structure clicked for me pretty quickly.”

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Netflix·Software Engineer·Onsite - Behavioral / Leadership

May 2026

Interviewed for a bizops role at Netflix, just one behavioral question from what I can tell. Pretty standard stuff but Netflix always feels like they want a specific kind of answer.

Tell me about a time you received feedback that actually changed how you work.

“I had a decent story ready but I think I spent too long on the setup and not enough on what actually shifted in my behavior afterward.”

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Netflix·Product Designer·Onsite - Product Sense / Strategy

May 2026

Netflix design interview, one question about building a visual search product. Pretty open-ended, felt like they wanted to see how you structure ambiguity more than whether you had the right answer.

How would you design a visual search feature for a large image-based platform?

“I went straight into user needs and worked outward from there.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026

Netflix technical screen for a software engineering role, basically one big graph problem that kept expanding in scope. The question started reasonable and then just kept going.

Given N lines of text describing task dependencies in various formats (like 'install A after B' or 'C->A'), implement a parser that builds a directed graph, returns a valid topological ordering, detects cycles and reports a minimal one, and handles malformed or duplicate input gracefully. Also analyze time and space complexity and discuss how you'd adapt the solution for streaming input or very large graphs.

“This was a lot.”

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Netflix·Software Engineer·Recruiter / HR Screen

May 2026

The Netflix HR screen was way more involved than I expected. It felt like a mini behavioral round, covering resume projects, motivation for leaving, and even the Netflix culture memo. Worth taking seriously.

Walk me through your resume projects and what your role was in each.
Do you have direct experience relevant to this role?
Why do you want to work at Netflix specifically?
What's prompting you to leave your current role?
Have you read the Netflix culture memo, and what are your thoughts on it?

“I thought this would be a quick warmup but they pushed on specifics pretty fast.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

May 2026

Netflix system design round, one big question about frequency capping for an ad platform. Dense topic with a lot of moving parts and I don't think I covered everything they wanted.

Design a frequency capping system for an advertising platform that limits how many times a user sees a given ad, campaign, or order within configurable time windows.

“This one sprawled in every direction the second I started drawing boxes.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

May 2026

Netflix product interview, one question deep into video feature strategy for a younger audience. Pretty open-ended and I wasn't totally sure how much to lean into the metrics side versus the discovery side.

A new video feature is being launched targeting young adults. How would you evaluate whether there's product-market fit, and what metrics would you use to define success?

“I spent too long on the PMF framing and not enough time being concrete about metrics.”

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Netflix·Product Manager·Hiring Manager Screen

May 2026

Interviewed at Netflix and got asked about pushing back on stakeholders. Pretty short experience to describe but it's the kind of question that sounds easy until you're actually in the room.

How do you handle saying no to stakeholders?

“I fumbled this a bit.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026

Netflix SWE interview with a string pairs problem that pushed past brute force. The interviewer wanted a real algorithmic solution, not just the naive O(n^2 * L) approach, so you had to actually think about bitmask encoding and complement enumeration.

Given a list of n strings, find the number of unique unordered pairs (i, j) where i < j such that strings[i] and strings[j] share no common characters. The interviewer wanted a solution better than the naive O(n^2 * L) approach.

“I started with the brute force just to show I understood the problem, which I think was fine, but I could tell they were waiting for me to push further.”

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Netflix·Data Scientist·Technical Phone Screen

May 2026

A deep-dive experimentation design question for a DS role at Netflix, basically a full A/B testing case study crammed into one prompt. The breadth was a lot to handle in real time and I definitely stumbled on a few parts.

For a new search ranking feature experiment, how would you choose the unit of randomization (user, session, or query level), and how does carryover or interference factor into that decision?
What primary success metric and guardrail metrics would you define for this search ranking experiment?
Given a baseline click-through rate of 10% and a target of detecting a 2% relative uplift, walk through the sample size calculation for a two-proportion z-test. Then explain how clustering or variance reduction techniques would change the required sample size.
How would you handle sample ratio mismatch checks, novelty effects, and sequential testing without inflating Type I error during experiment execution?
How would you pre-register and test for heterogeneous treatment effects across segments like head vs tail queries, country, or device type, while controlling for false discovery?
If the ranking change affects supply and demand dynamics in search, how would you redesign the experiment to handle interference, and what are the tradeoffs between cluster randomization and switchback testing?
How would you define stop/go criteria for rollout, and how do you prevent the experiment's exposure from contaminating future ML training data?

“My instinct was to jump straight to user-level and call it a day, but they pushed back immediately.”

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