Senior Content Engineer, KnowledgeOps

• 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: Git authoring, CI/CD builds, linting, link and code-sample validation, and publishing
• Make knowledge AI-ready: structure and package content for RAG, internal assistants, and agentic workflows, including llms.txt, MCP-accessible sources, agent skills and context kits, and evaluation sets for answer accuracy
• 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

• 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: 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
• Enough technical depth to read code and APIs, verify accuracy, and write working examples
• A systems mindset: you fix the class of problem, not just the instance
• 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
• Documentation experience in graphics, simulation, robotics, or real-time systems; familiarity with USD, Omniverse, Isaac Sim, or ROS
• 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

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 144,000 USD - 230,000 USD for Level 4, and 176,000 USD - 276,000 USD for Level 5. You will also be eligible for equity and benefits .

Applications for this job will be accepted at least until October 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.