{"id":2464287,"url":"https://alion.io/job/lancedb-documentation-engineer","title":"Documentation Engineer","company":{"id":4364,"name":"Lancedb","domain":"lancedb.com","url":"https://alion.io/company/lancedb","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":88,"open_postings":11,"ghost_share":0,"stale_share":0.273,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Product","role_family":"Product","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":180000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Git","optional":false},{"name":"LanceDB","optional":false},{"name":"LLM","optional":false},{"name":"Midjourney","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"Runway","optional":false},{"name":"SQL","optional":false},{"name":"World Models","optional":false},{"name":"Machine Learning","optional":true},{"name":"Rust","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-13T14:49:47Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T06:24:59Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About LanceDB\nAI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles.\nAbout the Role\nAs the Documentation Engineer at LanceDB, you will make Lance and LanceDB easier to understand, adopt, and use in production. You'll work at the intersection of engineering, technical writing, and developer experience, turning complex data infrastructure concepts into clear explanations and working examples.\nYou'll partner closely with engineering, product, and our open-source community to document new features, build runnable examples, and keep guidance accurate as the product evolves. This role will own documentation as part of the product, with a particular emphasis on content that both developers and AI agents can discover, understand, and use reliably, while also building the tooling that keeps it accurate.\nThis is an opportunity to define how documentation supports a growing ecosystem of AI-assisted development and have a meaningful impact on how developers and AI agents learn and build with LanceDB.\nWhat You'll Do\nCore documentation: Create and maintain quickstarts, tutorials, conceptual guides, API references, and troubleshooting documentation for Lance and LanceDB.\n\nDocumentation for AI agents: Design documentation agents can use reliably, including structured API descriptions, explicit prerequisites, complete examples, and clear error and recovery guidance. Evaluate it through agent-driven tasks to find where missing context or ambiguous instructions prevent agents from completing workflows correctly.\n\nRunnable examples: Build examples and sample applications that demonstrate workflows such as data ingestion, vector search, filtering, indexing, and multimodal data management.\n\nAI-assisted workflows: Use AI coding assistants and other AI tools to research the codebase, draft and revise content, generate examples, and automate maintenance, verifying outputs against product behavior and tested code.\n\nDocumentation tooling: Maintain documentation tooling and automate checks for code examples, API references, links, and content quality.\n\nPartner closely with engineering and product teams to document new features alongside releases and keep existing guidance aligned with product changes.\n\nDevelop and continuously improve documentation structure, navigation, and search so developers can find the right information at each stage of their journey.\n\nUse community questions, support feedback, and documentation usage to identify gaps and prioritize improvements.\n\nRepresent LanceDB with open-source contributors, reviewing documentation changes and establishing clear contribution guidelines.\n\nWhat We're Looking For\n8+ years of experience in documentation engineering, developer-focused technical writing, software engineering, or a related role\n\nA proven track record of technical documentation, shown through a portfolio that explains complex concepts clearly and helps developers complete real tasks\n\nExperience designing documentation for consumption by AI agents, with an understanding of how content structure, context, and API clarity affect agent performance\n\nDemonstrated fluency with AI tooling, including coding assistants and LLM-based workflows, and sound judgment about how to validate their outputs\n\nStrong Python skills and the ability to write, run, and debug examples using APIs and SDKs\n\nFamiliarity with databases, SQL, and data workflows, plus an interest in learning vector search and multimodal AI infrastructure\n\nExperience with Git, pull requests, Markdown, and documentation workflows integrated with software development\n\nDemonstrated ability to learn a product by reading code, experimenting with APIs, and collaborating with engineers\n\nStrong editorial judgment, attention to technical accuracy, and the ability to prioritize and work independently\n\nNice to Have\nExperience documenting databases, data infrastructure, or AI and machine learning tools\n\nFamiliarity with LanceDB, Lance, Apache Arrow, or related technologies\n\nExperience with TypeScript or Rust and documenting APIs across multiple languages\n\nExperience with documentation platforms, generated API references, and CI-based testing of code examples\n\nExperience building agent workflows, documenting tool interfaces, or evaluating agents against developer tasks\n\nContributions to open-source projects or 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