Multimodal AI Engineer, Document Understanding

LlamaIndex•San Francisco, CA

1d•Hybrid

About The Position

Join us and help shape the future of AI by redefining document workflows with AI agents. 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. Your work will directly impact thousands of developers building RAG applications and document agents, while also contributing to our open-source frameworks that shape how the industry approaches document processing. 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. We're hiring multiple people and will work with you to find the best fit.

Requirements

3-7 years of experience in machine learning engineering or applied research
Strong software engineering fundamentals with production Python experience (modern tooling: uv, ruff, mypy, Pydantic)
Hands-on 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

Nice To Haves

Experience with vision-language models, transformer architectures, or model fine-tuning (LoRA, QLoRA)
Experience building evaluation frameworks, benchmarks, or data quality pipelines
Experience with model serving frameworks (vLLM, TensorRT, ONNX) or MLOps tools
Experience specifically with document understanding, OCR, or layout analysis
Contributions to open-source ML projects or frameworks
Experience with LLM applications and RAG systems
Strong understanding of model optimization techniques (quantization, distillation, pruning)
Experience with Docker/Kubernetes and distributed systems
Active participation in ML research community

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 integrate ML innovations into production APIs
Contribute to both our open-source frameworks and enterprise offerings
Drive technical decisions while balancing research exploration with product delivery