Sitefire helps brands make sure their products show up when people ask artificial intelligence for recommendations. They figure out what content AI models read, and automatically create or update web pages so the brand gets mentioned more often. If you ask ChatGPT 'What is the best electric car?' and BMW shows up in the answer, Sitefire is the type of tool BMW would use to make that happen.
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questions shown from the full report
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backed by first-hand evidence, not inference
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sources cited and linked below
An AI agent searched public sources for how Sitefire actually interviews — engineering blogs, job posts, and first-hand accounts from people who interviewed there. A result only counts as evidence if the page carries real content; logins, paywalls and robots-controlled pages are never read. Questions labelled Inferred are derived from proxy signals rather than direct accounts. Read more about what counts as evidence.
YC W26 startup building a Generative Engine Optimization (GEO) and agentic marketing suite. Tech stack includes TypeScript, React, Vite, Supabase, Vercel, and ClickHouse. Processing large datasets (~50M+ rows) to provide AI visibility analytics and automate marketing workflows.
While exact interview steps for Sitefire are not public, typical YC founding engineer processes involve an initial behavioral screen with the founders to assess autonomy, a deep-dive system design or architecture discussion, and a highly practical take-home or live repo-based coding session where the use of AI tools is strongly encouraged.
Each question carries the confidence we place in it and links to the source it came from, so you can check the reasoning rather than trust it.
Sitefire is building the marketing suite for the Agentic Web. A founding engineer must understand the core domain of how AI agents source information differently than traditional search engine crawlers.
Founding engineer roles require immense autonomy. Interviewers will probe your ability to work without detailed specs, move fast, and own your technical choices.
The job description explicitly mentions working with 50M+ rows of data and building an analytics and execution layer. You need to show how you would connect Vercel, Supabase, and ClickHouse to an LLM.
The company uses React, Vite, and Supabase. Take-home assignments for early-stage startups in this stack heavily favor building functional CRUD apps to prove basic execution speed.
With 1 YoE for a founding engineer title at an AI-native startup, traditional LeetCode is unlikely. However, AI startups often test practical algorithmic thinking related to model routing and cost optimization. Evidence for formal DSA is thin.
This page shows the 5 strongest questions from the research. It deliberately omits everything that helps you actually pass:
Running the research yourself, for your own role, stack and rounds, costs under $0.50.
Researched by Interview Resources and published on 25 July 2026. Questions are predictions from public evidence, not a leaked question bank — see all companies.