Google Product Manager, Gemini Interview Experience
Google · Senior · Product Manager
I’d never had to do vibe coding live before. In the product design interview, they gave me 10 to 15 minutes to actually build a rough prototype, and most of what they were probing on was how I prompted and reprompted the tool.
Interview process
I got in through a referral for an L7 PM role on NotebookLM inside Gemini. The process included a recruiter screen, a conversational hiring manager deep dive, and an on-site split into two phases: first, product design and execution; then, technical system design and a leadership behavioral.
The unusual part was a live-coding section during the product design round, where I had to prototype the thing I had just proposed, and most of the pushback was about how I prompted and reprompted. Overall, it was one of the most practical AI PM loops I have done, and it matched what the recruiter told me almost exactly. I made it to the final round but did not get the offer.
Interview rounds · 5
- 1
Recruiter screen
BehavioralPortfolio ReviewArtificial IntelligenceI had a pretty standard 30 minute recruiter screen that was mostly checkboxy and high level, not technical in any deep way.
Q1. What are your compensation expectations?
How they answeredI told them I honestly had not thought about it yet, which is what I usually do that early. They wanted me to give some input first, but we just agreed to circle back later and it did not turn into anything weird.
Q2. Can you walk me through a complex technical project you've worked on and your experience using AI in products and in the product development process?
How they answeredI kept this pretty high level because that is the level the recruiter was operating at. I talked through a complex technical project, my experience with AI in the actual product, and also AI in the product development process, plus the tools I had used. There was not much deep follow-up. It felt like they had a set of questions and were just taking notes.
Follow-up questions- What tools did you use?
- 2
Phone screen
Portfolio ReviewProduct StrategyArtificial IntelligenceThe hiring manager screen was a 45 minute deep dive with a NotebookLM lead, and it felt conversational rather than scripted.
Q1. Walk me through one product you've worked on end to end.
How they answeredI went deep on one product I had worked on and walked through the full end to end process. They pushed more than the recruiter on the tools I used, how I worked with them, how I decide what is actually a good use case for an LLM, and what is not. Then we got into tradeoffs and alternative paths I considered. It was a normal hiring manager deep dive, just a little more AI specific.
Follow-up questions- What tools did you work with and how did you use them?
- How do you decide what is a good task for an LLM versus not?
- What tradeoffs did you make?
- Did you consider any other approaches when solving this problem?
Q2. How should we be thinking about differentiating ourselves relative to competitors?
How they answeredThis was the one I had not really planned for at the hiring manager stage. I treated it as a strategy conversation and gave my view on how they should position themselves relative to competitors, then got more specific on what NotebookLM should be doing inside the broader Gemini portfolio. It felt like they wanted to see whether I had an opinion on the space and could connect product strategy back to the actual product.
Follow-up questions- What role should NotebookLM play within the broader Gemini suite?
- 3
Final / onsite round
Product DesignExecutionAnalyticalArtificial IntelligenceStatistics & ExperimentationMy first onsite block was two very practical rounds, product design with a live vibe coding section and then an execution case, and both felt collaborative and very tied to the actual job.
Q1. Design an AI assistant for Gemini for college students.
How they answeredI approached it like a normal product sense question first, using a mission, users, pain points, solution flow, then narrowed to something I could actually prototype. In the last 10 to 15 minutes I had to live build a rough UX prototype with whatever tool I wanted. The interviewer mostly poked at my prompting, why I phrased things a certain way, what I would reprompt, and what I might be missing. It felt less like coding and more like showing that I can prompt and reprompt effectively under time pressure.
Follow-up questions- Build a prototype or MVP for the solution you just described.
- Why did you prompt it this way?
- Are there other ways you could prompt it, or is there something missing?
Q2. Users are complaining that Gemini is confident but wrong. How would you fix this?
How they answeredI treated this like an execution and debugging problem, not a product design question. I framed the problem, defined what success would actually look like, and laid out a north star, leading metrics, and guardrails. A big part of my answer was how to separate model capability problems from UX problems using both qualitative and quantitative signals. They explicitly told me not to design a fix, so the whole conversation was really about structured measurement, experimentation, and tradeoffs.
Follow-up questions- How would you define goals and measure success?
- How would you distinguish between model issues and user experience issues?
- How would you A/B test a fix?
- When would you roll back the model versus iterate on it?
- 4
Technical round
System DesignTechnicalArtificial IntelligenceThe technical round was a practical AI system design interview, more in the weeds than most PM loops but still very grounded.
Q1. Design a high-level system for Gemini responding to a user query.
How they answeredI walked through the high level system for how Gemini would handle a user query and return a response. The feel of it was much more practical than abstract. They were clearly testing whether I actually understand the moving pieces in an AI product, not whether I can recite some generic system design framework. If you know the stack, it is manageable, but it is definitely more technical than a typical PM loop.
- 5
Final / onsite round
BehavioralCross-FunctionalThe last round was a straightforward leadership behavioral focused on how I work through ambiguity and with skeptical technical stakeholders.
Q1. Describe a time you had to make a decision without enough data.
How they answeredI used one of my prepared examples and focused on how I made the call with incomplete information, what signals I still had, and how I thought through the tradeoffs. It was very standard behavioral territory.
Q2. Describe a time you disagreed with engineers or researchers.
How they answeredI answered this by talking through how I formed my opinion, held my ground, and used the right data points to make the case. The follow-up was really about whether I can persuade a skeptical technical partner without being rigid and while still acknowledging tradeoffs.
Follow-up questions- How do you convince a skeptical technical partner?
Q3. Describe a situation where you managed stakeholders with conflicting priorities.
How they answeredI used a standard stakeholder management example and walked through how I handled conflicting priorities and aligned people around a path forward. This one was very normal and felt like something I had already prepped for.
Tips from the candidate
I would go in already using these tools a lot. If you are not actively using AI tools in your day to day, it is hard to fake the fluency because it shows up across product sense, execution, and technical, not just in the vibe coding part. For the live build, I would have a really simple prompt template ready so I can turn my product sense notes straight into a first prompt and spend less time waiting on generation. I would also show up with actual opinions on the space, because they will ask strategy questions like how Gemini or NotebookLM should position against competitors.
Company culture
My read was that Gemini is very engineering- and research-driven, to the point that the product can feel a bit in the back seat compared with researchers, tech leads, and LLM engineers. They also seem very explicit that PMs are expected to be hands-on with AI tools, including vibe coding, and the interview process enforces that. At the same time, the process itself felt really well run: standardized, accurate to recruiter prep, yet still conversational and collaborative rather than robotic. It also felt a little Google-y to me, in that engineering really seems to run the show, just much more focused on AI work.