About the role
Join us and help shape the future of AI by defining the narrative around document understanding.
About
the Role We are looking for an AI Research Engineer to join our document understanding team. This role is ideal for someone who sits between applied research and strong engineering. You will work on vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training. The goal is simple: make our document AI systems more accurate, faster, and more cost-effective in production. You should be excited by frontier AI work, but equally motivated by practical product impact. This is not a pure research role where ideas stay in papers. You will be expected to prototype quickly, evaluate rigorously, and help turn promising approaches into production systems used by customers. What You’ll Do Develop and train vision-language models for document processing and document understanding. Build data pipelines for data curation, synthetic data generation, labeling, and benchmark creation. Evaluate base models and perform post-training or fine-tuning to hit specific performance targets. Improve model accuracy, latency, and cost-effectiveness across real-world document workflows. Design and maintain benchmarks to measure extraction quality, layout understanding, OCR performance, reasoning accuracy, and end-to-end system reliability. Work with messy real-world documents, including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents. Collaborate with engineering to move successful research prototypes into production. Work directly with customers when needed to translate product
requirements into benchmarks, experiments, and model improvements. Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems. Use modern AI coding workflows and tools to move quickly. What We’re Looking For 3–7 years of experience in machine learning engineering, applied research, or research engineering. Strong ML foundation, including hands-on experience benchmarking and training models. Strong Python skills and comfort with modern ML tooling, especially PyTorch. Experience with computer vision, vision-language models, NLP, document AI, OCR, extraction, or agentic AI systems. Ability to build experiments, evaluate results, and iterate quickly toward measurable performance improvements. Strong engineering judgment and ability to write clean, production-quality code. Comfort working in a fast-paced startup environment with high ownership and limited structure. Adaptable, scrappy, and self-directed — someone who can figure things out without waiting to be told. Strong technical writing and communication skills.
