Netflix L5 Software Engineer, Ads Interview Experience
Netflix · Senior · Software Engineer
The recruiter literally told me the system design prompt was frequency capping, and even with that heads-up the real test was whether I could talk about ads like I actually live in that world. If you get the terminology half wrong, it feels like a red flag immediately.
Interview process
The whole process was really fast for me, about three weeks end to end, and the ads org was super snappy about scheduling and responses. The recruiter reached out because my background lined up with what they want, and it felt obvious from the start that this team cares a lot more about ads domain fluency than generic big tech interview performance. I had a recruiter screen, a TTL cache phone screen, and then a five-round onsite with one coding round, two behavioral EM rounds, data modeling, and system design. The technicals were mostly straightforward if I could talk through tradeoffs, but the behaviorals were a much harder vibe check than I expected. I am still waiting to hear back, but the biggest thing I took away is that if I can speak confidently about how ads serving, demand, targeting, and capping actually work, they will forgive more than they would at other companies.
Interview rounds · 6
- 1
Recruiter screen
BehavioralI had a pretty standard recruiter flow, but it moved fast. There was an initial screen about my background, then a second call just to walk me through the loop. The standout part was they straight up told me the system design topic would probably be frequency capping, which was super helpful.
Q1. Can you walk me through your experience?
How they answeredI talked through my ads background, especially my multiformat ads experience and the fact that I know the publisher side well. I got the sense that was exactly why they reached out in the first place. The whole thing felt less like a deep sell and more like them checking whether my background matched what the ads org is staffing for.
Q2. What compensation are you expecting for this role?
How they answeredI gave a number based on Levels and said I was seeing around 500 for this role. The recruiter was fine with it and didn't push back. In hindsight I probably should not have anchored that early, but that is how the conversation went.
- 2
Phone screen
CodingTechnicalMy first technical was a 45 minute CodeSignal on a TTL cache. It was not very LeetCode-y. I wrote the basic solution, and then most of the round turned into follow-up discussion about tradeoffs, eviction, and where the implementation breaks.
Q1. Build a TTL cache.
How they answeredI wrote a straightforward TTL cache in JavaScript, then we talked through the gotchas. I said if I keep everything in memory and never really evict, the cache just keeps growing. For lazy eviction, the same issue shows up if expired entries are never cleaned up. I suggested regular compaction or cleanup on a cadence. The vibe was less, can you grind code, and more, do you understand the failure modes of something you'd actually build.
Follow-up questions- What happens if you keep everything in memory?
- You're evicting the cache lazily. What happens if you don't evict?
- What are some ways to get around that?
- 3
Final / onsite round
Data Structures & AlgorithmsCodingThe coding round in the onsite was the one that felt the least domain-specific. I got a tree traversal problem built around an org chart, and it honestly caught me off guard because I expected something more practical. The interviewer helped me quite a bit, so it ended up feeling collaborative.
Q1. Given an org chart tree, compute each employee's level, identify balanced employees, and build a histogram of employees by level.
How they answeredThe org chart was a tree of employees and direct reports. One part was computing each person's level, another was deciding whether someone was balanced by comparing how many people were above them versus below them, and the last was building a histogram by level. I initially thought about solving each part separately, but the interviewer nudged me toward doing it in one loop. I was shaky here, and he basically steered me to the final shape of the solution.
Follow-up questions- Can you solve the three parts in one traversal?
- 4
Final / onsite round
BehavioralCross-FunctionalI had two back-to-back EM behavioral rounds, and this was the hardest vibe check of the whole process. The format was very tell-me-about-a-time and much more Amazonian than culture-memo-heavy. They were clearly testing how I work with PMs and other functions, not just whether I can code.
- Q1. Tell me about a complex project.
- Q2. Tell me about a risk you took.
- Q3. Tell me about a time you navigated ambiguity.
Q4. Tell me about a time you influenced a product decision.
How they answeredThis was the one I really screwed up. I reached for a failed project from my resume, but I had forgotten a bunch of the details, so I kind of mumbled my way through it and never really landed the story. It was supposed to be about how I helped shape a product decision with PM, but I felt totally unprepared and honestly felt like I was about to have a heart attack while answering.
Follow-up questions- How did that go with your product team?
- Q5. How do you deal with disagreements and conflict?
Q6. Why do you want to work at Netflix?
How they answeredI gave my usual why Netflix spiel, and they pretty much just moved on. It felt like a checkbox, not a deep motivation conversation.
- 5
Final / onsite round
Data ModelingTechnicalAnalyticalThe data modeling round was basically, do you actually know the ads business well enough to represent it cleanly. It was much less about generic schema tricks and much more about naming the right entities and speaking the domain language correctly. If you are even a little sloppy on terminology, I think it hurts you here.
Q1. What data models do you need for an advertiser to start a campaign and show ads to users?
How they answeredI framed it around the core business objects you need to run ads: advertiser setup, campaigns, the ad or line item itself, targeting data, frequency cap settings, and revenue or event records. I had prepped this really hard off the job req, so I could talk through the entities coherently instead of hand-waving. This round felt like pure domain fluency.
Follow-up questions- How do you model targeting?
- How do you model frequency capping?
- How do you capture revenue numbers?
- 6
Final / onsite round
System DesignTechnicalThe system design was my favorite round because it was concrete and very close to the actual job. The prompt was exactly what the recruiter hinted at: frequency capping. The challenge was not generic distributed systems trivia, it was knowing the ads-specific details well enough to make the design believable.
Q1. How would you design a frequency capping system?
How they answeredI treated it a lot like a rate limiter plus ad event ingestion. I talked through enforcing caps like five times a day or ten times in two weeks, and I leaned on click or impression tracking patterns for the event side. The big nuance I called out was granularity: you can cap at the line item, campaign, or category level. Google Ad Manager docs were actually really useful for those specifics.
Follow-up questions- How would you support limits like five impressions per day or ten in two weeks?
- What granularity would you cap at?
- How would you ingest the ad events needed to enforce it?
Tips from the candidate
If I were telling a friend how to prep, I would say spend way more time understanding the ads business than grinding random interview questions. I literally took the job req, broke it down team by team, and mapped what each team probably owns, what services they touch, and what data models they care about. Read Google Ad Manager docs, especially around buying, targeting, and frequency capping, because the details matter. Then do enough DSA questions to not get surprised if one interviewer throws in a toy tree problem anyway. Also get your behavioral stories tight, because I learned the hard way that one fuzzy story can make you feel terrible even if the rest of the loop is strong.
Company culture
On the ads side, they feel extremely bullish right now and they are clearly staffing up hard. The process was fast, the recruiters were responsive, and I got the sense they are running a ton of interviews. What surprised me is that the loop did not feel very classic Netflix to me. There was not much culture memo probing, and the behavioral style felt way more like Amazon, which makes sense because I heard they pulled in a lot of people from Amazon Ads. The signal they seem to care most about is not just whether I am technically strong, but whether I already speak the ads domain fluently enough to be useful right away.