Senior Content Engineer, KnowledgeOps

Details

Build the systems that deliver Simulation Technology knowledge to developers, partners, internal teams, and AI agents. The KnowledgeOps team defines the information architecture and quality standards for Omniverse, Isaac Sim, and OpenUSD documentation. This role builds the automation, pipelines, and AI-ready infrastructure that apply those standards at scale and works with documentation leads to refine the architecture based on implementation feedback.

The ideal candidate is a writer who builds: comfortable with Git, CI, Python, style guides, and daily use of LLMs. The team creates systems where the right approach is easier than the wrong one by turning documentation, standards, and tribal knowledge into tooling, templates, and self-service workflows.

Responsibilities

• Collaborate with documentation leads on knowledge architecture, providing implementation feedback to refine content models, information architecture, and AI-readiness patterns.
• Build and operate docs-as-code pipelines, including Git authoring, CI/CD builds, linting, link and code-sample validation, and publishing.
• Structure and package content for retrieval-augmented generation (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, including drafting, updating, auditing, freshness checks, code-accuracy checks, and terminology-drift detection, with human review where judgment is required.
• Encode standards for user stories, release readiness, onboarding, and security guidance into templates, validators, and guided workflows.
• 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, using the data to determine what to build, fix, or retire.
• Partner with Engineering, Product, Security, Developer Relations, and Partner Programs so every release is documented, discoverable, and supportable.

Requirements

• Bachelor's degree or equivalent experience, plus 8 or more years of experience in technical writing, developer documentation, or developer experience for complex software platforms.
• Hands-on docs-as-code experience with Git, pull request review, Markdown or reStructuredText, static site generators such as Sphinx, MkDocs, or Docusaurus, and CI/CD.
• Working proficiency in Python or a similar language for automating content tasks, transforming data, and integrating with APIs.
• Practical experience applying LLMs to content workflows, including prompting, retrieval and grounding, and output evaluation, with sound judgment about where AI can be trusted.
• Information architecture skills, including content modeling, taxonomy, and metadata for navigation and machine retrieval.
• Sufficient technical depth to read code and APIs, verify accuracy, and write working examples.
• A systems mindset focused on solving classes of problems rather than individual instances.
• A collaborative approach to translating design intent into working systems and providing clear, constructive implementation feedback.

Preferred Qualifications

• 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.
• Experience building content-quality automation, such as Vale or custom linters, documentation test harnesses, or freshness monitoring.
• Measurable reductions in support load or onboarding time through self-service knowledge systems.
• Open-source contributions or published work related to docs-as-code, AI-assisted documentation, or knowledge engineering.

Compensation and Benefits

The base salary range is $144,000–$230,000 USD for Level 4 and $176,000–$276,000 USD for Level 5. Base salary is determined by location, experience, and the pay of employees in similar positions. The role is also eligible for equity and benefits.

Applications will be accepted at least until October 20, 2026. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.