Netflix interview notes, page 3 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·Recruiter / HR Screen

Apr 2026

Recruiter screen for a Software Engineer role on Netflix's advertising team. Non-technical, 30 minutes, focused on culture fit and background. A coding phone screen follows if this goes well.

Why are you looking for a new role right now?
Walk me through your background and the work you've done over the past several years.
Tell me about a significant challenge you faced at work and how you dealt with it.
Pick a value from Netflix's culture memo and tell me how you've actually demonstrated it.
Netflix values candor. Tell me about a piece of feedback you received, how you took it, and what you actually changed afterward.
Tell me about a time you disagreed with a teammate or your manager. What did you do?
What are your compensation expectations?
Describe a time you had to prioritize ruthlessly when you were under pressure and resources were tight.

“I'd been at my current place long enough that this question felt almost philosophical.”

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Netflix·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Netflix EM interview, one question about how others perceive you, both the good and the bad. Pretty standard behavioral territory but Netflix has a way of making it feel higher stakes than it should.

What do your coworkers say about you? Share both positive and critical feedback you've received.

“I fumbled this a bit because I went too vague on the negative side.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Netflix system design round, one big question about ads audience targeting that sprawled into like five different sub-problems. More breadth than I expected for a single session.

Design an ads audience-targeting system that supports attribute-based audience definitions, bulk uploads of millions of user IDs, and real-time targeting decisions at ad-serve time.

“This thing kept expanding.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026

Netflix coding interview, sliding window problem on arrays/strings. Pretty standard algorithmic round but the O(n) constraint means you can't brute force your way through it.

Given an array or string, find the length of the longest contiguous subarray where all elements are unique. They wanted an O(n) solution.

“I knew sliding window was the move but fumbled the part about tracking last-seen positions with a hash map versus just a set.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026

Netflix coding interview for a software engineer role. One algorithmic problem, graph-based, with a twist on a classic problem I'd seen before but not quite in this form.

Given n tasks with dependencies between them and a unique completion time per task, find the minimum total time needed to finish all tasks.

“Recognized it as a topological sort problem pretty fast, which felt good, but then I fumbled a bit because the variable completion times per task threw me off from the version I'd practiced.”

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Netflix·Data Scientist·Technical Phone Screen

Apr 2026

Netflix DS interview, product analytics focus. The main question was a meaty A/B testing design problem around an onboarding redesign, and it had enough moving parts that I felt like I was juggling too many things at once.

The product team wants to launch a redesigned onboarding flow and needs evidence it actually increases activation. Design an A/B test for this: walk through your hypothesis, how you'd randomize, which metrics you'd track as primary vs. guardrail, how you'd calculate runtime, and then tell me how you'd make a launch decision if early results show a positive lift in activation but a spike in support tickets.

“There's a lot packed into this.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026

Netflix coding screen for a software engineer role. One algorithmic problem, felt pretty focused on sliding window logic with a twist around episode grouping.

Given a list of (episode_id, day) pairs, an integer K, and an integer T, return True if any K-day sliding window contains at least two episodes whose IDs differ by at most T. Otherwise return False.

“The sliding window part clicked pretty fast but the 'same season' condition tripped me up for a bit.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026

Netflix data engineering interview that picked up from a SQL pipeline exercise and immediately went deep on productionizing it. The follow-up questions came fast and covered a lot of ground, felt less like a conversation and more like a checklist being run through.

Given the SQL pipeline you just built, what would you need to address before putting it into production?
How would you approach data quality checks for a production pipeline?
How would you decide on a partitioning strategy, and how does that choice affect query performance?
How do you detect and handle data skew in joins or aggregations?
How would you make your pipeline idempotent and handle late-arriving data or backfills?
What does your approach to SLA definition, monitoring, and alerting look like for a data pipeline?

“This was the umbrella question and I treated it too broadly at first.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Netflix system design round, one big open-ended question about crash-resilient file systems. No coding, just whiteboarding trade-offs for about an hour. Felt like a production engineering deep-dive more than a typical design interview.

Design a resilient file system that can correctly recover file contents and metadata after a sudden crash like a power loss or kernel panic. Walk through how you'd support basic operations like create, read, write, delete, and rename, and define what durability guarantees your design provides.
Where are the bottlenecks in your design, and what would you change to make this production-ready at scale?

“This one is deceptively wide.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Netflix ML infra system design round. The whole thing was basically one big question about building a compute scheduler for ML workloads, and they wanted you to cover a lot of ground fast. Dense but interesting.

Design a compute resource allocation system that schedules ML training and inference jobs across heterogeneous CPU and GPU resources in multiple clusters. Cover the job submission API, queueing, priority, resource matching, bin-packing, fairness, cluster placement, preemption, fault tolerance, and utilization tracking.

“This is basically a full distributed systems design question dressed up as an ML infra problem.”

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