
10,000+ employees
Founded 1993
🏥 Healthcare
🏭 Manufacturing
🤖 Artificial Intelligence
Healthcare • Manufacturing • Artificial Intelligence
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
🔥 0 minutes ago
🗽 New York, Oregon, +3 more states – Remote
💵 $144k - $276k / year
⏰ Full Time
🟠 Senior
✍️ Content Marketing Manager
🦅 H1B Visa Sponsor
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10,000+ employees
Founded 1993
🏥 Healthcare
🏭 Manufacturing
🤖 Artificial Intelligence
Healthcare • Manufacturing • Artificial Intelligence
NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.
• 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
• 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
• Equity • Benefits
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