LlamaIndex
Full TimeProduce technical content at weekly velocity while building real benchmarks and running experiments in document AI. You'll need production Python experience, strong software engineering fundamentals, and deep knowledge of ML techniques in computer vision, NLP, or multimodal learning. Writing clarity, research synthesis ability, and comfort shipping quickly matter most. Work in-person in San Francisco.
Written from this posting by Neural Jobs AI. The full description is below.
Join us and help shape the future of AI by defining the narrative around document understanding.
We are seeking a highly technical ML engineer who can produce compelling, authentic technical content at high velocity. You will combine deep expertise in document AI with strong writing skills to build benchmarks, publish technical analyses, and establish our position as the definitive leader in document understanding.
This is not a traditional DevRel or Marketing role. You will write real code, build real benchmarks, and run real experiments - then translate that work into published content at a pace far faster than academic publishing. Your output will directly drive awareness and adoption among the developers building the next generation of document-powered applications.
Design, build, and maintain comprehensive benchmarks for document parsing and understanding
Publish high-quality technical content at a weekly cadence (blog posts, benchmark reports, technical comparisons, tutorials)
Stay deeply current with the document AI landscape - new models, papers, competitors, techniques
Run experiments and translate findings into publishable artifacts quickly
Produce technical analyses that demonstrate our capabilities against alternatives
Contribute to open-source examples, notebooks, and documentation
Collaborate with the core ML team to surface improvements and capabilities worth highlighting
Engage authentically with the developer community through technical content (not conferences/events)
Experience in software engineering (ML engineering + research a bonus)
Strong software engineering fundamentals with production Python experience
Understanding of modern ML techniques, particularly in computer vision, NLP, or multimodal learning
Demonstrated ability to write clearly, quickly, and authentically about technical topics
Bias toward shipping - comfortable publishing at blog pace, not paper pace
Ability to read, understand, and synthesize research papers rapidly
Scrappy and self-directed - can identify what's worth writing about and execute end-to-end
Track record of high-velocity output in fast-paced environments
Experience with vision-language models, transformer architectures, or document AI specifically
Existing portfolio of technical writing (blog posts, tutorials, technical documentation)
Experience building evaluation frameworks or benchmarks
Familiarity with OCR, layout analysis, table extraction, or document structure understanding
Active presence in ML/AI technical communities
Experience with LLM applications and RAG systems
In-person in San Francisco.
Shape the Narrative: Your content will define how developers think about document understanding. You'll have direct influence on market perception.
Technical Credibility: Work with cutting-edge document AI systems processing millions of documents. Your benchmarks and analyses will be grounded in real capabilities.
High Autonomy: Significant freedom to identify what matters and publish quickly. No lengthy approval chains.
Growth Opportunity: Help build this function from the ground up as we scale.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
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LlamaIndex is a data framework for connecting large language models to private and structured data, enabling retrieval-augmented generation and agentic workflows over enterprise documents.
Founded in 2022, it offers both an open-source framework and a managed platform.
140K - 220K Yearly
United States Hybrid
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