Senior Content Engineer, KnowledgeOps

This is a fully remote position, open to applicants in New York, +4 more states.

📋 Description

• Collaborate with documentation leads to enhance knowledge architecture and improve content models, information architecture, and AI-readiness patterns.

• Develop and manage docs-as-code pipelines that include Git authoring, CI/CD builds, linting, link and code sample validation, and publishing.

• Organize and package content for Retrieval-Augmented Generation (RAG), internal assistants, and agentic workflows, including llms.txt, MCP-accessible sources, agent skills, context kits, and evaluation sets.

• Automate the content lifecycle using LLMs and scripting, covering everything from drafting and updates to auditing.

• Establish checks for content freshness, code accuracy, and terminology consistency, incorporating human review where necessary.

• Encode standards within templates, validators, and guided workflows.

• Capture organizational knowledge and transform it into playbooks, decision guides, and self-service pathways.

• Evaluate time-to-first-success, support deflection metrics, recurring inquiries, and the quality of AI-generated answers.

• Collaborate with Engineering, Product, Security, Developer Relations, and Partner Programs to ensure that releases are thoroughly documented, easily discoverable, and fully supportable.

⛳️ Requirements

• Bachelor's degree or equivalent professional experience.

• Over 8 years of experience in technical writing, developer documentation, or enhancing developer experience for complex software platforms.

• Practical experience with docs-as-code methodologies using Git, pull request reviews, Markdown/RST, static site generators like Sphinx, MkDocs, or Docusaurus, along with CI/CD.

• Proficient in Python or similar languages for automating content tasks, data transformation, and API integration.

• Hands-on experience applying LLMs to content workflows, including prompting, retrieval, grounding, and output evaluation.

• Strong judgment regarding the reliability of AI applications.

• Skills in information architecture, including content modeling, taxonomy, and metadata for navigation and machine retrieval.

• Technical expertise to read code and APIs, ensure accuracy, and create working examples.

• Systems-oriented mindset aimed at addressing classes of problems.

• A collaborative approach with the ability to offer constructive, implementation-focused feedback.

• Experience with RAG pipelines, AI assistants, MCP servers, or agent skill and context libraries that are supported by documentation.

• Background in documentation for graphics, simulation, robotics, or real-time systems; familiarity with USD, Omniverse, Isaac Sim, or ROS.

• Experience in developing content quality automation tools such as Vale, custom linters, doc test harnesses, or freshness monitoring systems.

• Demonstrable reductions in support load or onboarding time through self-service knowledge systems.

• Contributions to open source projects or published work related to docs-as-code, AI-assisted documentation, or knowledge engineering.

🏝️ Benefits

• Equity

• Benefits