KnowledgeOps Engineer

We are seeking a KnowledgeOps Engineer to join our team. You will build the systems that deliver Simulation Technology knowledge to developers, partners, internal teams, and AI agents. Your primary focus will be on setting the information architecture and quality standards for Omniverse, Isaac Sim, and OpenUSD documentation.

Responsibilities:

• Collaborate with documentation leads on knowledge architecture, sharing implementation feedback to refine content models, information architecture, and AI-readiness patterns
• Build and run docs-as-code pipelines using Git, CI/CD builds, linting, link and code-sample validation, and publishing
• Make knowledge AI-ready by structuring and packaging content for RAG, internal assistants, and agentic workflows
• Automate the content lifecycle with LLMs and scripting, from drafting and updating to auditing
• Encode standards into templates, validators, and guided workflows rather than static documents
• Capture tribal knowledge from engineering, product, and operations, and turn it into playbooks, decision guides, and self-service paths
• Measure outcomes such as time-to-first-success, support deflection, repeat questions, and AI answer quality
• Partner with Engineering, Product, Security, Developer Relations, and Partner Programs to ensure every release ships documented, discoverable, and supportable

Requirements:

• Bachelor's degree or equivalent experience, plus 8+ years in technical writing, developer documentation, or developer experience for complex software platforms
• Hands-on docs-as-code experience with Git, pull request review, Markdown/RST, static site generators, and CI/CD
• Working proficiency in Python or similar to automate content tasks, transform data, and integrate with APIs
• Practical experience applying LLMs to content workflows and sound judgment about where AI can be trusted
• Information architecture skills, including content modeling, taxonomy, and metadata
• Enough technical depth to read code and APIs, verify accuracy, and write working examples
• A systems mindset and collaborative style

Nice to Have:

• Experience building RAG pipelines, AI assistants, MCP servers, or agent skill and context libraries backed by documentation
• Documentation experience in graphics, simulation, robotics, or real-time systems
• Content quality automation you've built, such as Vale or custom linters, doc test harnesses, or freshness monitoring
• Measurable reductions in support load or onboarding time through self-service knowledge systems
• Open source contributions or published work on docs-as-code, AI-assisted documentation, or knowledge engineering

Salary: 144,000 USD - 230,000 USD for Level 4, and 176,000 USD - 276,000 USD for Level 5