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Level: Senior-Level
Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 360 min · Interviewer: Very Friendly
Topics: Data Modeling, System Design, Algorithms, Behavioral Questions, Online/Offline Paths, Message Queues, Redis, Ad Tech, Topological Sort
Location: San Francisco Bay Area
Interview date: 2026-01-15
This post describes my interview experience with Netflix's advertising department. Because of the current popularity of the department, and because I plan to accept the offer, I won't share specific questions. The questions I encountered were very similar to those asked in the last three months.
Phone Screen: The phone screen involved a question on topological sort.
Onsite Interview: The onsite interview questions were similar to those I had seen in previous posts. The most common coding questions include topological sort, system design (frequency capping), and data model questions, followed by two rounds of behavioral questions. The rounds were split across two days.
Coding (Topological Sort): While not a direct copy of any specific problem, the coding question's essence was the same as topological sort. I needed to keep in mind the runtime and be prepared for variations, such as sorting multiple results or printing all results, along with their respective runtimes.
System Design (Frequency Capping): This section explored the depth of my knowledge. If the interviewer didn't delve deep enough, I took the initiative to elaborate further. For instance, I discussed how Redis is implemented, the different modes it offers, how it handles traffic, and how atomicity is achieved (and the reasons for doing so). I covered both online and offline paths (read/write). For the offline path, I mentioned which message queue to use and how to create keys. I also touched on how to implement specific requirements, such as strictly enforcing a single ad cap.
Data Model: I shared what I've done in the past or learned through GPT, ideally covering all the main advertising components: ad campaigns, business accounts, creatives, and user targeting. I included as much as I could think of and discussed how these components are related. I also discussed the overall process of the advertising platform, including the intake flow, serving flow, and impression tracking flow. I expanded on the intake portion, though this might not have been necessary, but served to demonstrate my understanding of the entire system.
Behavioral Questions: These two rounds focused on my work experience and how well it matched the job description. The interviewers (managers) wanted to ensure I could start contributing immediately. The behavioral questions were standard, and I don't think these rounds were designed to be eliminatory. The manager rounds were essentially with potential supervisors interested in me. If you know which team or position you're interviewing for, thoroughly review the job description and ensure you have experience covering at least 80% of the requirements. When discussing your experience, emphasize the relevant aspects. The managers primarily want candidates with relevant experience who can start working right away.
I received the results in about two days. Because my conversation with the manager went well, the process was relatively quick. The work I'd be doing aligned with my existing experience, so the manager essentially indicated that as long as I passed the other rounds, I would be hired.
Additional Experience: The advertising department is currently hiring across the board, using its own question bank and evaluation criteria. The process is somewhat open-book and not particularly difficult. If there are areas I'm unfamiliar with, I should use GPT to prepare. The main goal is to find candidates with relevant experience who can start working immediately. It's easier to pass if the position and team align with my experience by at least 80%. The manager round seems to be the most crucial; if it goes well, you're likely to get the job as long as the position is available.