Senior Machine Learning Engineer
medici land governanceEl Paso, TX
Senior Machine Learning Engineer
medici land governanceEl Paso, TX
yesterday
Occupations
Computer Systems Engineers/ArchitectsSoftware DevelopersComputer and Information Research ScientistsAbout 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court documents, property records, and other complex scanned files across both clean new recordings and poor-quality historical documents.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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JOB OVERVIEW
Experience level
Manager
Location
El Paso, TX
Occupation
Computer Systems Engineers/Architects
Industry
Software Publishers
Posted
yesterday
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