---
title: 'Documentation Engineer at LanceDB'
canonical: 'https://feeny.ai/job/documentation-engineer-lancedb-united-states-canada-f28jc38958g5'
type: 'job'
last_seen: '2026-10-18'
---

# Documentation Engineer at LanceDB

- **Company:** LanceDB
- **Location:** United States / Canada
- **Compensation:** $180k–$250k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-13
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/lancedb/ec98cf2b-3d82-432b-a4e3-806b97baf81c

## Job description

## About LanceDB

AI 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.

## About the Role

As 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. You'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. This 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.

## What You'll Do

- Core documentation: Create and maintain quickstarts, tutorials, conceptual guides, API references, and troubleshooting documentation for Lance and LanceDB.
- Documentation 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.
- Runnable examples: Build examples and sample applications that demonstrate workflows such as data ingestion, vector search, filtering, indexing, and multimodal data management.
- AI-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.
- Documentation tooling: Maintain documentation tooling and automate checks for code examples, API references, links, and content quality.
- Partner closely with engineering and product teams to document new features alongside releases and keep existing guidance aligned with product changes.
- Develop and continuously improve documentation structure, navigation, and search so developers can find the right information at each stage of their journey.
- Use community questions, support feedback, and documentation usage to identify gaps and prioritize improvements.
- Represent LanceDB with open-source contributors, reviewing documentation changes and establishing clear contribution guidelines.

## What We're Looking For

- 8+ years of experience in documentation engineering, developer-focused technical writing, software engineering, or a related role
- A proven track record of technical documentation, shown through a portfolio that explains complex concepts clearly and helps developers complete real tasks
- Experience designing documentation for consumption by AI agents, with an understanding of how content structure, context, and API clarity affect agent performance
- Demonstrated fluency with AI tooling, including coding assistants and LLM-based workflows, and sound judgment about how to validate their outputs
- Strong Python skills and the ability to write, run, and debug examples using APIs and SDKs
- Familiarity with databases, SQL, and data workflows, plus an interest in learning vector search and multimodal AI infrastructure
- Experience with Git, pull requests, Markdown, and documentation workflows integrated with software development
- Demonstrated ability to learn a product by reading code, experimenting with APIs, and collaborating with engineers
- Strong editorial judgment, attention to technical accuracy, and the ability to prioritize and work independently

## Nice to Have

- Experience documenting databases, data infrastructure, or AI and machine learning tools
- Familiarity with LanceDB, Lance, Apache Arrow, or related technologies
- Experience with TypeScript or Rust and documenting APIs across multiple languages
- Experience with documentation platforms, generated API references, and CI-based testing of code examples
- Experience building agent workflows, documenting tool interfaces, or evaluating agents against developer tasks
- Contributions to open-source projects or experience supporting a developer community

## About LanceDB

## Core Business
- **Primary industry**: AI data infrastructure / vector databases / multimodal lakehouse
- **Target customers**: B2B – AI/ML teams, enterprises building generative AI, recommendation systems, and large-scale training pipelines.
- **Mission/purpose statement**: “Build Better Models, Faster” by providing a unified foundation to accelerate training dataset development.

## Products & Services
- **Embedded Vector Database**: Open-source vector database for multimodal AI, scaling to billions of embeddings with minimal management. Supports vector/semantic, full-text, and hybrid search combined with SQL filters.
- **Multimodal Lakehouse (LanceDB Enterprise)**: A unified platform for curation, feature engineering, retrieval, and training at massive scale. Includes automated pre-processing, versioning, branching, and rollback.
- **LanceDB Cloud**: Managed cloud service with auto re-indexing, caching, and a management dashboard – no server management required.
- **Lance Format**: A modern columnar data format optimized for AI training, analytics, and retrieval – significantly faster than Parquet and WebDataset.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total funding $41.1M; annual revenue $1.2M (LinkedIn estimate)
- **Notable Investors/Partners**: Theory Ventures (led Series A), CRV, Swift Ventures, Y Combinator.
- **Growth Signals**:
  - Headcount: 32 employees (+135% YoY, +9.3% monthly growth)
  - Customers include Midjourney, Runway (testimonials on lancedb.com)
  - 11,382 LinkedIn followers (+60.1% yearly)
  - 6 active job postings; monthly job posting trend +20%

## Competitive Advantages
- **Open-source core** with a permissive license, enabling community adoption and vendor lock-in avoidance.
- **Lance columnar format** – purpose-built for AI workloads, offering fast random access, column appends without rewriting, and 70% Model FLOPS Utilization (MFU) during training.
- **Multimodal by design** – stores vectors, images, tensors, video, and metadata in a single table, replacing 3–4 different data stores.
- **SSD-based ANN index** – scales beyond memory for low-latency billion-scale vector search on a single node.

## Strategic Focus
- Accelerate development of the Multimodal Lakehouse (announced with Series A)
- Expand enterprise features (managed cloud, security, compliance)
- Grow ecosystem integrations (Spark, Python UDFs, etc.)
- Scale go-to-market and customer success (hiring Forward Deployed Engineers, Solutions Engineers)

## Why Work Here
- **Culture**: Startup environment with deep open-source roots; founders are former pandas co-author (Chang She) and HDFS/core contributor (Lei Xu). Engineering-heavy team (38% technical).
- **Remote/hybrid policy**: Not explicitly stated; likely flexible given distributed team (US, Canada, China) and startup nature.
- **Notable perks/engineering culture**: Work on foundational AI infrastructure used by Midjourney and Runway. Opportunities to contribute to open-source, build in Rust, and shape the multimodal data ecosystem. Fast-growing company with strong investor backing.

## Sources
1. [lancedb.com](https://www.lancedb.com/)
2. [crunchbase.com](https://www.crunchbase.com/organization/lancedb)
3. [linkedin.com](https://www.linkedin.com/company/lancedb)
4. [ycombinator.com](https://www.ycombinator.com/companies/lancedb)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/lancedb)

## Other roles at LanceDB

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- [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-lancedb-san-francisco-0btn7w423bah) — San Francisco, CA
- [AI Research Engineer](https://feeny.ai/job/ai-research-engineer-lancedb-united-states-kzr7s7kn5xsw) — United States
- [Senior Product Manager](https://feeny.ai/job/senior-product-manager-lancedb-united-states-canada-kddh90xzsr7s) — United States / Canada
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- [Senior Customer Success Engineer](https://feeny.ai/job/senior-customer-success-engineer-lancedb-united-states-canada-2vht5p4k14na) — United States / Canada