# Senior Content Engineer, KnowledgeOps at NVIDIA

NVIDIA is hiring a Senior Content Engineer, KnowledgeOps for a remote role open to applicants in United States. It pays $144k-$230k a year and Level rates it Works on AI ●●●○; you can [apply on Level](https://jobsbylevel.com/go/ca67fe25-1d27-4d0e-9265-e1eeec7fbd37?utm_medium=ai-agent).

AI Level 3, AI centrality 68 out of 100. Remote (US, Remote).

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 3 (score 68 out of 100)
- Location: Remote (US, Remote)
- Salary: $144k-$230k
- Posted: October 16, 2026
- Apply: https://jobsbylevel.com/go/ca67fe25-1d27-4d0e-9265-e1eeec7fbd37?utm_medium=ai-agent

## Prepare for this job

The job page lists 4 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-content-engineer-knowledgeops-at-nvidia-163a6e

## Description

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. You'll 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. You're a writer who builds: as comfortable in Git, CI, or Python as in a style guide, and fluent with LLMs in daily work. We build systems where the right way is easier than the wrong way. 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…?" What you'll be doing: 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 What we need to see: 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 Ways to stand out from the crowd: 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 description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-content-engineer-knowledgeops-at-nvidia-163a6e

Source: https://jobsbylevel.com/jobs/senior-content-engineer-knowledgeops-at-nvidia-163a6e

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