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court documents, property records, and other complex scanned files across both clean new recordings and poor-quality historical documents.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