other
Posted Nov 21, 2025Member of Technical Staff, Document Understanding
at LlamaIndex
San Francisco, United StatesHybrid
Responsibilities
- RESPONSIBILITIES: - Develop, train, and optimize machine learning models for document structure understanding, table extraction, layout analysis, and multimodal content processing - Build robust data pipelines, evaluation frameworks, and experimentation infrastructure - Design and implement production ML systems that handle complex, real-world documents at scale - Stay current with latest advances in vision-language models, document AI, and multimodal learning - Collaborate with engineering teams to
- Shape your role based on your interests and strengths. ADDITIONAL
Requirements
- Join us and help shape the future of AI by defining the narrative around document understanding.
- ABOUT THE ROLE: We are seeking exceptional AI engineers to join our core document understanding team.
- You will work at the intersection of computer vision, natural language processing, and production ML systems to push the boundaries of what's possible in document parsing and understanding.
- Our document understanding team builds the intelligence behind LlamaParse, LlamaExtract, and our other processing products.
- These systems are processing millions of complex documents including PDFs, PowerPoints, Word documents, and spreadsheets.
- Depending on your background and interests, you might focus more on data curation and evaluation, model fine-tuning and experimentation, or ML infrastructure and production systems.
- experience in machine learning engineering or applied research - Strong software engineering fundamentals with production Python
- experience training, fine-tuning, or deploying ML models in production - Deep understanding of modern ML techniques, particularly in computer vision, NLP, or multimodal learning -
- Experience with at least one of: data pipeline development, model training/fine-tuning, or ML infrastructure - Ability to read and implement from research papers and technical specifications - Track record of executing with high intensity in fast-paced environments - Strong technical communication skills and comfort with open-source collaboration PREFERRED QUALIFICATIONS: -
- Experience with vision-language models, transformer architectures, or model fine-tuning (LoRA, QLoRA) -
- Experience with model serving frameworks (vLLM, TensorRT, ONNX) or MLOps tools -