Member of Technical Staff, Document Understanding

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. 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. 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 REQUIRED QUALIFICATIONS: - 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 PREFERRED QUALIFICATIONS: - 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 LOCATION: We offer a hybrid-friendly culture based out of our downtown San Francisco office. Remote candidates will be considered for exceptional fits. WHY JOIN US? - Impactful Mission: Work on innovative AI products that redefine how knowledge is accessed and utilized. Your models will process millions of documents and directly impact thousands of developers. - Cutting-Edge Technology: Work with the latest vision-language models, contribute to open-source frameworks used industry-wide, and shape the future of document AI. - Collaborative Team: Join a focused team of passionate engineers and researchers committed to pushing the boundaries of what's possible in document understanding. - Technical Autonomy: Significant creative