Applied Machine Learning Engineer

Lead Applied Machine Learning Engineer – Document Intelligence

Company: Medici Land Governance

Employment Type: Full-Time

Level: Lead / Principal

About the Role

Medici Land Governance is looking for a Lead Applied Machine Learning Engineer to own and advance our document intelligence capabilities for property records, legal documents, and public-record data.

Our platform processes large and diverse document collections ranging from clean modern recordings to decades-old historical records with degraded scans, complex layouts, handwriting, stamps, legal descriptions, tables, and inconsistent formatting.

We are expanding our use of proprietary machine learning models and domain-specific training data to improve how these documents are classified, restored, read, understood, and converted into structured information.

This role will lead the technical direction for our document ML systems, including model development, fine-tuning, training data strategy, evaluation, computer vision, OCR, multimodal models, and production inference.

This is a deeply hands-on role for someone who wants to own a difficult real-world machine learning problem from data and experimentation through production deployment and continuous improvement.

What You’ll Own

• Technical direction for our document intelligence and applied ML systems
• Proprietary and domain-specialized document models
• Training, validation, and ground-truth datasets
• Document classification and quality assessment
• Image preprocessing and document restoration
• OCR and text recognition
• Layout and document structure understanding
• Structured information extraction
• Vision-language and multimodal model evaluation
• Fine-tuning and model adaptation
• Confidence scoring and validation
• Model evaluation and regression testing
• Production ML inference and optimization
• Continuous improvement using production failures and feedback

What You’ll Do

• 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.

What We’re Looking For

• 6+ years building production machine learning or applied AI systems. &lt