Own the end-to-end developer and agent-facing enablement for CUDA-Q, turning research needs into surfaces, docs, tests and templates that accelerate adoption and accuracy across researchers, engineers, and AI agents.

• Analyze developer journey needs with product teams and domain experts to identify gaps for both human and agent workflows using CUDA-Q.
• Define practical standards for developer surfaces including GitHub, docs, example code, and onboarding that work for both developers and the AI agents they use.
• CreateMCP servers, Agent Skills, API documentation patterns, agent-consumable tests, and prompt-ready templates.
• Build and maintain enablement resources — templates, runbooks, checklists, context files, and reference implementations — that quantum researchers and developers can use directly.
• Evaluate content performance using human engagement metrics and agent signals to continuously improve developer time-to-value with CUDA-Q.
• Define success criteria and evaluation frameworks for agentic developer tools — designing benchmarks, running evals, and translating results into actionable product improvements.
• Track emerging AX, GEO, and AI citation research and translate findings into practical guidance for teams building quantum computing applications