Senior Content Engineer, KnowledgeOps

NVIDIA · Remote, United States

Build the systems that deliver Simulation Technology knowledge to developers, partners, internal teams, and the AI agents that increasingly sit between them and our documentation. Our KnowledgeOps team sets the information architecture and quality standards for Omniverse, Isaac Sim, and OpenUSD documentation. Youll build the automation, pipelines, and AI-ready infrastructure that apply those standards at scale, and work with documentation leads to refine the architecture based on what you learn.
Youre a writer who builds: as comfortable in Git, CI, or Python as in a style guide, and fluent with LLMs in daily work. For you, that means turning documentation, standards, and tribal knowledge into tooling, templates, and self-service paths, so fewer people have to ask "how do I…?"
Collaborate with documentation leads on knowledge architecture, sharing implementation feedback (what works, breaks, and scales) to refine content models, information architecture, and AI-readiness patterns
Build and run docs-as-code pipelines: Make knowledge AI-ready: structure and package content for RAG, internal assistants, and agentic workflows, including llms.Automate the content lifecycle with LLMs and scripting, from drafting and updating to auditing, with checks for freshness, code accuracy, and terminology drift, and human review where judgment matters
Encode standards (user stories, release readiness, onboarding, security guidance) 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, and use the data to decide what to build, fix, or retire
Partner with Engineering, Product, Security, Developer Relations, and Partner Programs so every release ships documented, discoverable, and supportable
Bachelors degree or equivalent experience, plus 8+ years in technical writing, developer documentation, or developer experience for complex software platforms
Git, pull request review, Markdown/RST, static site generators (Sphinx, MkDocs, Docusaurus, or similar), 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 (prompting, retrieval and grounding, output evaluation) and sound judgment about where AI can be trusted
Information architecture skills: content modeling, taxonomy, and metadata for both navigation and machine retrieval
A collaborative style: you turn design intent into working systems and give leads clear, constructive feedback grounded in implementation
Experience building RAG pipelines, AI assistants, MCP servers, or agent skill and context libraries backed by documentation
Content quality automation youve built, such as Vale or custom linters, doc test harnesses, or freshness monitoring
Open source contributions or published work on docs-as-code, AI-assisted documentation, or knowledge engineering
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 20, 2026.

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