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Netflix interview notes, page 18 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·Technical Phone Screen

Jul 2026
Netflix phone screen for a software engineering role that turned into a system design conversation pretty quickly. The prompt was vague enough that I misread the whole direction at first, which set a slightly awkward tone for the rest of the call.
  • Design a system to back up files. Assume the core file operations already exist. How would you structure the backup, ensure the destination mirrors the source directory, scale the system, and handle failures?

“My first instinct was to start designing classes and I went down that path for maybe two minutes before they redirected me.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Netflix system design round for a software engineer role, focused entirely on building a relational data model for a demand-side ad platform. Dense question with a lot of moving parts, and I felt like I was playing catch-up the whole time.
  • Design a normalized relational schema for a demand-side advertising platform, covering advertisers, campaigns, ad groups, and creatives, along with targeting, budgeting, pacing, scheduling, and status fields. Also explain how you'd structure reporting join keys to trace impressions and clicks back up the hierarchy.

“This one sprawled in a way I didn't expect.”

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Netflix·Data Scientist·Recruiter / HR Screen

Jul 2026
Netflix data scientist interview that was basically one big compensation negotiation exercise. You had to justify a number, defend trade-offs across different pay structures, and then actually roleplay pushing back on a lowball offer. More rigorous than I expected for something that sounds like an HR conversation.
  • What is your total compensation target for an all-cash offer at this level and location? Give a target range and a walk-away floor, backed by at least two market sources, and explain your assumptions about the role's scope.
  • Walk through the trade-offs between base salary, signing bonus, performance cash, title, scope, start date, and flexibility. How do you weigh these against each other?
  • How does your compensation expectation change if the role's scope increases or decreases by roughly 10%? What about if the benefits package is below market?
  • Roleplay a negotiation: the initial offer comes in 15% below your target. How do you respond? Include a conditional concession tied to something measurable.

“This tripped me up more than it should have.”

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Netflix·Software Engineer·Technical Phone Screen

Jul 2026
Netflix ML infra screen, one coding question the whole time. The problem was deceptively clean on the surface but the rotation constraint is where things get interesting. Felt okay about my solution but never heard back on the outcome.
  • Design a service with two API methods: one to add or update a title's relevance score, and one to return the top title for a user's homepage. The catch is that the same title can't be returned on two consecutive calls if any other title exists.

“The score ordering part is easy, just a heap or sorted structure.”

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Netflix·Software Engineer·Hiring Manager Screen

Jul 2026
Interviewed at Netflix for a SWE role and got a question about their culture memo that I thought I'd prepared for but kind of fumbled in the moment.
  • What do you like most about the Netflix culture memo, and what would you have changed about it?

“I'd read the memo before the interview but not deeply enough to have a real opinion.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jul 2026
Netflix system design round, one big question about ads frequency capping. Spent the whole session on it and it went pretty deep into territory I wasn't fully prepared for.
  • Design a frequency-cap system for ads: at the time an ad is served, determine whether a user has already seen a specific ad, campaign, or advertiser more than a configured limit within a given time window. Address the data model, how you'd store and update counters, keeping reads under 10ms, handling high write throughput, eventual consistency, multi-region replication, and what happens when the cap store goes down.

“This one sprawled in ways I didn't anticipate.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Netflix ML Engineer screen that went pretty deep on optimizer theory. One question, but it had a lot of surface area and they clearly wanted more than a textbook answer.
  • Walk me through the differences between Adam and SGD, including how each update rule works, memory overhead, convergence behavior, and when you'd pick one over the other.

“I started with the mechanics, SGD is just gradient times learning rate, with momentum as an optional velocity term that smooths out the updates.”

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Netflix·Data Scientist·Technical Phone Screen

Jul 2026
Netflix Data Scientist interview with a meaty ML system design question around fraud detection. One question, but it had a lot of surface area to cover and I definitely felt the pressure trying to hold all the pieces together at once.
  • Walk through the full ML workflow for building a fraud detection model on an online payments product, covering data collection through deployment and monitoring. How do you deal with severe class imbalance and concept drift?

“This question sprawls in every direction and I underestimated how much they wanted on the monitoring side.”

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Netflix·Software Engineer·Recruiter / HR Screen

Jul 2026
Standard Netflix HR screen for a software engineering role. Mostly background and motivation stuff, with a notable focus on their culture memo.
  • Why do you want to join Netflix?

“They really do want you to tie this back to their culture memo specifically.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Netflix ML engineer interview that went deep into NLP fundamentals and modern LLM architecture. Pretty technical throughout, no fluff questions, they clearly wanted someone who actually understood the internals of these models and not just how to call an API.
  • What is SentencePiece and how does it work under the hood?
  • Which tokenizers do BERT and typical Transformer-based language models use, and what's the reasoning behind those choices?
  • Walk me through the core components inside a Transformer block and what each one does.
  • Compare a standard Transformer to LLaMA and Qwen architectures. What design choices differ and what are the trade-offs, specifically around things like MoE, RMSNorm, and RoPE?

“I knew the high level answer but stumbled a bit on the specifics of the training algorithm.”

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