Decagon is one of the leading AI-native customer support platforms — autonomous agents that resolve tickets end-to-end at companies like Notion, Eventbrite, and Bilt. Founded by ex-OpenAI engineers. The interview is selective and reflects the high engineering bar of frontier AI startups.
Process
Recruiter screen → 60-minute coding pair → 60-minute system design → 60-minute past-project deep dive → behavioral. Cycle: 3–4 weeks.
What they actually ask
- Design an AI agent system that resolves customer support tickets autonomously
- Design knowledge ingestion and retrieval for company-specific support documents
- Design evaluation framework that measures autonomous resolution quality
- Coding: practical TypeScript/Python, often with concurrency or LLM-tooling flavor
- Past-project deep dive: must demonstrate genuine engineering depth
Levels and comp (2026)
- SE: $200K–$260K total
- Senior SE: $290K–$390K
- Staff: $420K–$560K
- Principal: $580K–$770K
Equity has high upside given Series B/C valuation and category leadership.
Prep priorities
- Be fluent in TypeScript/Python and at least one LLM API
- Understand RAG, agent loops, and eval frameworks
- Read papers and engineering posts on agent design (Anthropic, OpenAI cookbooks)
Frequently Asked Questions
Is Decagon remote-friendly?
Hybrid in San Francisco; some remote within US for senior roles.
How does Decagon compare to Sierra, Ada, or Forethought?
Sierra is the most prominent rival; Ada is the older incumbent; Forethought has been pivoting. Decagon and Sierra are the highest-comp; Decagon’s technical bar is famously demanding.
What is the engineering bar?
Very high. The team is small and the standards reflect frontier AI lab norms. Strong technical writing, demonstrable craft, and AI-native fluency are essential.