Applied Machine Learning Engineer

Job Description

Lead Applied Machine Learning Engineer – Document Intelligence

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Company: Medici Land Governance

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Employment Type: Full-Time

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Level: Lead / Principal

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About the Role

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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.

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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.

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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.

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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.

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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.

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What You’ll Own

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

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

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• 6+ years building production machine learning or applied AI systems.
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• Strong experience in computer vision, OCR, document AI, multimodal ML, NLP, or information extraction .
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• Experience training or fine-tuning machine learning models rather than exclusively integrating commercial AI APIs.
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• Strong Python and PyTorch experience.
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• Experience developing and curating training and ground-truth datasets.
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• Strong understanding of transformer-based vision, language, or multimodal architectures.
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• Experience with model evaluation, error analysis, experimentation, and regression testing.
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• Experience deploying ML models into production.
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• Understanding of GPU inference, memory utilization, batching, latency, throughput, and model optimization.
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• Ability to analyze model failures and systematically improve performance.
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• Ability to establish technical direction and drive execution in an ambiguous environment.
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Relevant Technologies

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Experience with some of the following is valuable:

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PyTorch, Hugging Face, OpenCV, LayoutLM, Donut, TrOCR, PaddleOCR, DocTR, Tesseract, Vision Transformers, Vision-Language Models, MLflow, Weights & Biases, vLLM, Triton, ONNX, TensorRT.

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Experience with every technology listed is not required.

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Strong Bonus Points

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• Property records, deeds, mortgages, liens, title records, court records, legal documents, or public-record experience.
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• Historical document digitization.
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• Degraded scan restoration.
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• Handwriting recognition.
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• Experience developing proprietary OCR or document-understanding models.
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• Fine-tuning multimodal or vision-language models on proprietary datasets.
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• Synthetic training-data generation.
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• Active learning or human-in-the-loop ML systems.
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• Model quantization, distillation, or optimization.
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• Self-hosted GPU inference.
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• Open-source contributions or research in computer vision, OCR, multimodal ML, or document intelligence.
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Who Thrives Here

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You’ll likely thrive in this role if you:

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• Want to develop and improve models, not simply call APIs.
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• Enjoy solving difficult problems with messy real-world data.
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• Care deeply about measurable accuracy.
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• Can combine classical computer vision with modern deep learning.
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• Are comfortable moving between experimentation and production.
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• Like investigating why individual documents fail.
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• Can make strong technical decisions and move quickly.
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• Want ownership of a critical machine learning platform.
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• Can lead technically while remaining hands-on.
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Who Is Not a Fit

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This role is unlikely to be the right fit if your experience is primarily:

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• Prompt engineering
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• ChatGPT wrappers
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• RAG applications without underlying model development
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• LangChain or agent development without deeper ML experience
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• Commercial AI API integration without model training or evaluation
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• Pure MLOps or infrastructure
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• Pure data engineering
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• Academic research without production deployment
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The Opportunity

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Property records present an unusually challenging document intelligence problem because quality, structure, terminology, and formatting can vary dramatically across jurisdictions and decades.

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This role will have significant ownership over how Medici applies machine learning to that problem and will help develop increasingly specialized models, datasets, and evaluation systems around our domain.

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The objective is a document intelligence platform that becomes more accurate, resilient, efficient, and specialized as it processes more of the records we work with.