Senior Machine Learning Engineer

Posted yesterday

medici land governanceDenver (CO)

Computer Systems Engineers/Architects Software Publishers

SENIORITY

Manager

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About the role

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court documents, property records, and other complex scanned files across both clean new recordings and poor-quality historical documents.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 owndocument intake, quality assessment, and routingimage preprocessing and scan enhancementOCR, VLM, and document extraction workflowsstructured extraction and indexingfallback across multiple AI providers and standalone OCR/model systemsevals, regression testing, and prompt/model versioningserving strategy for batch and real-time workloadslatency, throughput, and memory optimizationconfidence 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-onWhat 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 designML/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 onSomeone 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

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