Applied Machine Learning Engineer

Medici Land Governance is developing machine learning solutions for property records, legal documents, and public-record data. The Lead Applied Machine Learning Engineer will own the technical direction of document intelligence systems, developing and fine-tuning models for classification, OCR, document understanding, structured extraction, evaluation, and production inference while leading a small AI/ML engineering team.

Responsibilities:

• Develop and improve machine learning models specialized for property records and public-record documents
• Fine-tune and adapt open-source computer vision, OCR, transformer, and vision-language models using domain-specific datasets
• Evaluate model architectures and determine when to fine-tune, distill, combine, or develop specialized models
• Improve document classification, OCR, layout understanding, and structured extraction accuracy
• Build systems capable of handling both clean modern recordings and difficult historical records
• Develop preprocessing strategies for skew correction, denoising, deblurring, contrast enhancement, orientation correction, cropping, binarization, and degraded scan recovery
• Improve recognition of faded text, handwriting, stamps, seals, unusual fonts, damaged pages, poor microfilm, and inconsistent layouts
• Develop structured extraction for document types, parties, recording information, parcel data, legal descriptions, mortgages, liens, releases, and other property-record information
• Design and maintain high-quality training, validation, and ground-truth datasets
• Build feedback loops that convert production failures and human corrections into improved training data
• Develop confidence scoring and validation systems that reduce incorrect or hallucinated results
• Build rigorous evaluation and regression-testing frameworks for model and pipeline changes
• Establish measurable standards for OCR accuracy, field extraction, document classification, hallucination rate, document-level accuracy, latency, throughput, and cost
• Deploy and optimize models for high-volume production inference
• Optimize GPU utilization, batching, memory consumption, concurrency, latency, and throughput
• Benchmark proprietary models against commercial OCR, document AI platforms, and leading multimodal models
• Provide technical leadership to a small AI/ML engineering team while remaining highly hands-on