AI Conversation Designer, Customer Support

Notion Notion · Enterprise · San Francisco, CA · Customer Experience

This role focuses on designing and executing the AI customer support journey, specifically for a chatbot and automated email system. It involves conversation design, intent architecture, knowledge management, QA, and leveraging data and generative AI for improvements within an LLM-based support stack. The primary output is the AI-powered customer support experience.

What you'd actually do

  1. Own the day-to-day execution and quality of chatbot intents and knowledge (build, QA, iterate).
  2. Own intent architecture end-to-end: taxonomy design, hierarchical intents, coverage mapping, and gap analysis.
  3. Operate a rolling QA program (intent performance review, regression checks, gap analysis) and drive refinements.
  4. Keep chatbot information and knowledge up to date to ensure accurate, consistent customer support.
  5. Support launches by implementing and quality testing new intents and knowledge within the chatbot.

Skills

Required

  • 3+ years of conversation design experience with in-depth knowledge of conversation design, UX copy, or linguistic content verification.
  • Demonstrated experience designing and improving chatbot experiences; using data to show the impact/ROI of those designs.
  • Strong operational rigor: you can run QA programs, maintain knowledge freshness, and manage a backlog of improvements.
  • Strong cross-functional execution skills: you can partner with SMEs and product/CX teams to ship against launch timelines.
  • You have a metrics mindset: you believe success is a measured outcome, and will dive deep into data to inform design and prioritization decisions.
  • You have a proven ability to deal with ambiguity in a rapidly changing business environment.
  • You have experience with Decagon, Ada, Sierra, or using generative AI for support automation with another chatbot.

Nice to have

  • Experience at a high-growth SaaS company
  • Experience using Notion
  • Some familiarity with Python or SQL

What the JD emphasized

  • best-practice SOP writing
  • AI Support roadmap
  • LLM-based support stack
  • quality depends on routing logic, retrieval configuration, evaluation/monitoring, and disciplined release/QA practices
  • metrics mindset
  • deal with ambiguity in a rapidly changing business environment

Other signals

  • AI customer support journey
  • AI Support roadmap
  • LLM-based support stack
  • conversation design
  • system configuration
  • routing logic
  • retrieval configuration
  • evaluation/monitoring
  • generative AI recommendations