Senior Machine Learning Engineer

Lead ML Engineer, Document Intelligence Platform We’re hiring a hands-on technical leader to own our end-to-end document intelligence platform. Our system processes court documents, property records, and other complex scanned files across both clean new recordings and poor-quality historical documents. We need someone who can lead architecture and execution across document quality assessment, preprocessing, OCR/VLM extraction, indexing, provider fallback, evals, and serving strategy. This is a high-ownership role for someone who can make strong technical decisions, improve production accuracy, reduce hallucinations, and build resilient systems in a fast-changing model landscape. What you’ll own document intake, quality assessment, and routing image preprocessing and scan enhancement OCR, VLM, and document extraction workflows structured extraction and indexing fallback across multiple AI providers and standalone OCR/model systems evals, regression testing, and prompt/model versioning serving strategy for batch and real-time workloads latency, throughput, and memory optimization confidence scoring, validation, and hallucination reduction What you’ll do Architect and improve document AI pipelines for court records, property records, and other difficult document sets Build systems that assess image quality and document complexity on arrival and determine the correct processing path Optimize preprocessing for skew correction, denoising, deblurring, contrast enhancement, binarization, orientation correction, cropping, and scan recovery Design robust fallback across multiple paid model providers and standalone OCR/VLM systems to reduce vendor dependency Evaluate platforms such as direct APIs, OpenRouter, Vertex AI, and self-hosted inference for resiliency, cost, and operational flexibility Assess paid API vs self-hosted economics, including infrastructure cost, memory usage, throughput, fine-tuning needs, and long-term maintainability Build or optimize self-hosted serving systems using vLLM or comparable inference stacks Support both offline batch workloads and lower-latency live endpoints, including queueing and prioritization tradeoffs Improve compatibility between OCR outputs, custom internal formats, and downstream indexing/extraction systems Build an evals framework using MLflow or equivalent tooling, including ground-truth datasets, regression testing, prompt versioning, and user-feedback integration Establish measurable standards for OCR accuracy, extraction accuracy, hallucination rate, indexing quality, latency, throughput, and cost per document Lead failure analysis and iterative improvement, especially for degraded historical records Provide technical leadership to a small AI engineering team while remaining deeply hands-on What we’re looking for 6+ years building production AI/ML systems Strong experience in document AI, OCR, computer vision, NLP, or information extraction Experience owning or materially improving intelligent document processing pipelines Experience evaluating multiple model providers, APIs, and document AI services in production Strong understanding of paid APIs vs open-source/self-hosted tradeoffs Hands-on experience with vLLM, Triton, TGI, or similar inference-serving systems Experience with tools such as Tesseract, PaddleOCR, Textract, Azure Document Intelligence, Google Document AI, LayoutLM, Donut, or similar Strong Python skills and experience with PyTorch, OpenCV, Hugging Face, and modern ML infra Experience building eval frameworks with regression testing, ground truth, versioning, and production monitoring Strong understanding of batching, scheduling, GPU memory, latency, throughput, and cost optimization Experience reducing hallucinations through validation, confidence scoring, schema enforcement, and guardrails Ability to lead technical direction and drive execution in an ambiguous environment Strong bonus points Court records, property records, legal documents, or public-records experience Historical document digitization or degraded scan handling Hybrid OCR + VLM + rules + human-review system design ML/LLM observability using MLflow, Langfuse, W&B, or similar Priority scheduling, queue design, and mixed batch/live inference systems Fine-tuning document models when vendor APIs are insufficient Experience leading a small team or owning a critical AI platform Who thrives here Someone who wants real ownership Someone who can make decisions and move Someone who is hands on Someone who likes messy data and operational complexity Someone who cares about accuracy, resiliency, speed, and cost Someone who can lead without hiding behind process Who is not a fit Someone looking for a pure research role Someone who only wants prompt engineering or prototyping Someone who has not owned production systems with reliability and cost constraints Someone who prefers narrow model work over full-platform responsibility Medici Land Governance Inc.