Senior Content Engineer, KnowledgeOps

NVIDIA

. Collaborate with documentation leads on knowledge architecture and refine content models, information architecture, and AI-readiness patterns .

Posted 10/16/2026full-timeRemote • United StatesSenior💰 $144,000 - $276,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in technical writing and developer documentation for complex software platforms, with a strong focus on automation, content lifecycle management, and information architecture. Proficient in leveraging AI and LLMs to enhance documentation processes and improve user experience.

Highest-signal resume keywords

Docs-As-Code ExperienceTechnical Writing ExpertisePython ProficiencyInformation Architecture SkillsExperience with RAG Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Technical WritingContent ModelingAutomation ScriptingMarkdown/RSTCI/CDAPI IntegrationContent Quality AutomationLLM ApplicationStatic Site GeneratorsCode Verification

Soft Skills

Collaborative StyleConstructive FeedbackSound Judgment

Tools & Technologies

GitSphinxMkDocsDocusaurusValeMCP ServersAI AssistantsUSDOmniverseROS

Certifications & Qualifications

Bachelor's Degree or Equivalent Experience

Industry Keywords

Developer DocumentationKnowledge ArchitectureContent LifecycleAI-Readiness PatternsSelf-Service Knowledge SystemsSupport Load ReductionOnboarding Time ReductionAgent SkillsDecision GuidesPlaybooks

Tech Stack

Tools & technologies

Open SourcePython

About the role

Key responsibilities & impact

Collaborate with documentation leads on knowledge architecture and refine content models, information architecture, and AI-readiness patterns
Build and run docs-as-code pipelines including Git authoring, CI/CD builds, linting, link and code-sample validation, and publishing
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
Automate the content lifecycle with LLMs and scripting from drafting and updating to auditing
Implement checks for freshness, code accuracy, and terminology drift, with human review where judgment matters
Encode standards into templates, validators, and guided workflows
Capture tribal knowledge and turn it into playbooks, decision guides, and self-service paths
Measure time-to-first-success, support deflection, repeat questions, and AI answer quality
Partner with Engineering, Product, Security, Developer Relations, and Partner Programs to ensure releases are documented, discoverable, and supportable

Requirements

What you’ll need

Bachelor's degree or equivalent experience
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 such as Sphinx, MkDocs, or Docusaurus, and CI/CD
Working proficiency in Python or similar 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
Sound judgment about where AI can be trusted
Information architecture skills including content modeling, taxonomy, and metadata for navigation and machine retrieval
Technical depth to read code and APIs, verify accuracy, and write working examples
Systems mindset focused on solving classes of problems
Collaborative style and ability to provide constructive, implementation-grounded feedback
Experience with 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, 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

Benefits

Comp & perks

Equity
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