Job Title

About the CompanyInsurance Quantified is a platform that enables P&C carriers to win more business and dramatically improve speed to market by transforming the underwriting process. At Insurance Quantified, we are modernizing commercial underwriting by equipping underwriters with smarter workflows, better data, and more time to focus on what matters mostevaluating and selecting risk. Our technology is designed to unlock the full potential of underwriting teams at P&C carriers, MGAs, and wholesalers, and our customers are already seeing measurable results. As we scale, were looking for sales professionals who thrive in dynamic environments and want to shape the future of underwriting. IQ has a proven track record with over 50 successful implementations, including seven of the fifteen largest commercial carriers and several major MGAs. The platform delivers results fast, often reaching production go-live within 90 days.About the RoleWe are looking for an experienced, product-focused automation engineer to drive and continuously improve our document automation systems and client facing data products. This role is accountable for increasing our overall automation rate prioritizing fast, reliable, and maintainable solutions first, and applying deeper ML techniques only when they meaningfully improve outcomes.You will work at the intersection of product, systems, and applied ML, owning automation behavior end-to-end: from ingestion through extraction, monitoring, iteration, and reliability in real customer workflows.This is not a research-focused role. Success is measured by automation coverage, system robustness, and customer impact not by model sophistication.ResponsibilitiesAutomation & System OwnershipOwn end-to-end document automation systems in production, from ingestion to structured outputs used by downstream underwriting and claims workflows.Be accountable for improving overall automation rate, balancing speed, accuracy, and operational reliability.Make pragmatic tradeoffs between templates, heuristics, rules, classical ML, and LLM-based approaches to ship value quickly.Identify failure patterns in production and proactively harden systems to reduce recurring issues and bug volume.Applied ML (When Its the Right Tool)Design, fine-tune, and deploy ML models for document understanding when simpler approaches are insufficient (e.g., OCR enhancement, NER, layout analysis, table extraction).Apply ML selectively and intentionally, favoring explainable, maintainable solutions over unnecessary complexity.Collaborate with product and engineering leadership to determine when deeper ML investment is justified.Pipeline & Integration EngineeringBuild and maintain robust pipelines for processing PDFs, images, and emails, ensuring predictable behavior in noisy, real-world conditions.Partner closely with full-stack engineers to integrate automation outputs into customer-facing and internal workflows.Improve system behavior under edge cases such as low-quality scans, handwritten annotations, and inconsistent document formats.Human-in-the-Loop & Feedback SystemsDesign and maintain human-in-the-loop workflows where manual corrections feed back into improving automation quality.Ensure feedback loops are operationally effective, not just theoretically correct.Reliability & MLOpsOwn production monitoring for automation systems, including data quality checks, performance degradation, and model drift.Maintain versioning of data and automation logic, ensuring changes are safe, traceable, and reversible.Participate in bug triage and root-cause analysis related to automation failures, with a focus on long-term system improvement.QualificationsExperience: 3+ years of professional experience in Machine Learning or Data Science, with at least 1 year focused on NLP or Computer Vision.Technical Proficiency: Strong programming skills in Python and familiarity with libraries such as PyTorch, TensorFlow, or Hugging Face Transformers.Document Intelligence: Proven experience working with document processing technologies (e.g., Tesseract, AWS Textract, Azure Form Recognizer, LayoutLM, Donut).Data Handling: Experience handling large datasets of unstructured text and images.Cloud Infrastructure: Familiarity with deploying models on cloud platforms (AWS, GCP, or Azure) using Docker and Kubernetes.Preferred SkillsInsurance Domain Knowledge: Familiarity with ACORD forms, SOV (Schedule of Values), or Loss Runs.Advanced NLP: Experience with Large Language Models (LLMs) and Prompt Engineering for zero-shot information extraction.Graph Neural Networks: Experience using GNNs for linking entities within complex document layouts.Math Foundation: Solid understanding of probability, linear algebra, and optimisation algorithms.Why work here?A mission-driven company at the forefront of transforming a foundational industry, 8 year track record of delivering successful customer outcomes, a leadership team with deep expertise and a collaborative mindset, room to growpersonally, professionally, and financiallyas we scale, competitive compensation and equity, along with strong benefits.Equal Opportunity StatementInsurance Quantified is an Equal Opportunity Employer. Our policy is clear: there shall be no discrimination on the basis of age, disability, sex, race, religion or belief, gender reassignment, marriage/civil partnership, pregnancy/maternity, or sexual orientation. We are an inclusive organization and actively promote equality of opportunity for all with the right mix of talent, skills and potential. We welcome all applications from a wide range of candidates. Selection for roles will be based on individual merit alone.