AI Content Engineer
LlamaIndex1 month ago
Base Salary
$140k - $220k/yr
Responsibilities
- Design, build, and maintain comprehensive benchmarks for document parsing and understanding.
- Publish weekly blog posts, benchmark reports, technical comparisons, and tutorials.
- Run experiments and translate findings into publishable technical analyses and artifacts.
- Track developments in document AI, including new models, papers, competitors, and techniques.
- Contribute to open-source examples, notebooks, and documentation.
- Collaborate with the core ML team to identify improvements and capabilities worth highlighting.
- Engage the developer community through authentic technical content.
Requirements
- Experience in software engineering, with ML engineering or research experience as a bonus.
- Strong software engineering fundamentals and production Python experience.
- Understanding of modern ML techniques, particularly computer vision, NLP, or multimodal learning.
- Ability to write clearly, quickly, and authentically about technical topics.
- Ability to read, understand, and synthesize research papers rapidly.
- Track record of high-velocity output in fast-paced environments.
- Preferred qualifications include experience with vision-language models, transformer architectures, document AI, evaluation frameworks, benchmarks, OCR, layout analysis, table extraction, document structure understanding, LLM applications, or RAG systems.
- Preferred qualifications include a portfolio of technical writing and an active presence in ML/AI technical communities.
Benefits
- In-person work in San Francisco.
- High autonomy and freedom to identify topics and publish quickly.
- Opportunity to build the technical content function from the ground up.
- Direct influence on how developers understand document AI and document-powered applications.
Tech Stack
Categories
About LlamaIndex
LlamaParse is the most accurate agentic OCR platform for production AI — purpose-built for the documents agents actually encounter in the real world. Unlike general-purpose models that guess at structure, LlamaParse is engineered for complex layouts, dense tables, handwritten annotations, and scanned pages. Every page is automatically routed to the optimal model, so accuracy and cost are optimized without manual configuration. Trusted by teams at Lovable, 8am, Tabs, KPMG, and others running document-intensive workflows across legal, finance, healthcare, and more.