Demo Experience Engineer, Technical Success

OpenAI OpenAI · AI Frontier · San Francisco, CA · Go To Market

This role focuses on making OpenAI products tangible and trustworthy for customers by building agentic prototypes, reusable systems, and self-service tools. The engineer will own demo readiness, convert customer needs into accelerators, and build internal agentic systems to automate demo processes, ultimately improving customer understanding and product readiness.

What you'd actually do

  1. Own end-to-end demo readiness for new and priority product capabilities, including environments, integrations, synthetic data, evaluations, reliability checks, and fallback paths.
  2. Build compelling prototypes, LLM agents, and reference flows that make complex capabilities tangible and connect them to clear customer outcomes.
  3. Convert recurring needs into reusable accelerators—including companion apps, templates, LLM Skills and Plugins, and self-service demo packages—that scale across customer-facing teams.
  4. Work alongside GTM partners on high-impact customer moments, unblock critical technical gaps, and identify the patterns that should scale.
  5. Build always-on internal agentic systems that automate demo preparation, validation, and asset maintenance for the Demo Experience team.

Skills

Required

  • engineering depth across software architectures, integrations, data, LLM agents, skills, plugins, or other AI-powered systems
  • product-minded engineering
  • building compelling demos, reusable technical assets, or tooling
  • understanding when a customer needs a bespoke solution and when it should become a shared platform capability
  • AI safety
  • responsible deployment

Nice to have

  • technical storytelling
  • GTM execution
  • experience with synthetic data
  • experience with evaluations
  • experience with reliability checks
  • experience with fallback paths
  • experience with LLM Skills and Plugins

What the JD emphasized

  • Own ambiguous, high-leverage problems end to end
  • building agentic prototypes
  • creating the infrastructure and self-service tools
  • make new capabilities understandable, demonstrable, and reusable quickly at scale
  • convert recurring needs into reusable accelerators
  • build always-on internal agentic systems
  • AI safety, responsible deployment

Other signals

  • building agentic prototypes
  • creating infrastructure and self-service tools
  • making new capabilities understandable, demonstrable, and reusable quickly at scale
  • converting recurring needs into reusable accelerators
  • building always-on internal agentic systems