Machine Learning Engineer - Document Intelligence & Applied GenAI

5+ years of Python experience Experience training, fine-tuning, and deploying traditional computer vision models for document intelligence tasks. Hands-on experience with document understanding frameworks and models (LayoutLM, Donut, DocFormer). Experience with modern vision-language models with OCR capabilities (DeepSeek-OCR, LightOnOCR-1B). Experience deploying and optimizing models using inference frameworks such as vLLM (preferred), TGI, TensorRT, or ONNX Runtime. Experience applying LLMs to document intelligence workflows. Strong understanding of coordinate systems and spatial reasoning for document analysis.

Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction. Define metrics, benchmarks, and validation strategies for document workloads. Design and curate high-quality datasets for training and validation. Create scalable preprocessing pipelines for various document formats. Train and fine-tune transformer-based models for document understanding. Optimize models for reliability, accuracy, and cost efficiency. Deploy ML models with modern inference runtimes. Build guardrails, monitoring, and fallback mechanisms for models. Develop retrieval and chunking strategies for document structures. Optimize end-to-end RAG pipelines. Partner with PMs, backend engineers, and designers to define AI opportunities.