Machine Learning Engineer - Document Intelligence

Design and develop the core Document Intelligence Platform as a Service, focusing on generic document entity extraction, entity resolution, and document classification using cutting-edge AI/ML techniques. Build and optimize LLM-based document parsing, NLP-driven features, and scalable pipelines for data preprocessing, training, and inference to handle high-volume document workloads. Conduct exploratory data analysis on diverse document datasets to inform model development and feature engineering. Collaborate with software engineers, product managers, and other ML teams; write clean, maintainable, and testable code; participate in design reviews, knowledge-sharing sessions, and hackathons. Required qualifications include deep ML expertise, NLP/LLM experience (including RAG architectures and agentic frameworks), strong Python and software-engineering practices, and an advanced degree in a quantitative field. The role offers a flexible work approach with a primary location in Pleasanton, CA, USA or Vancouver, Canada, with hybrid/remote options and a base salary range of $160,000–$240,000 USD annually, plus potential bonuses and stock grants.