Specialty Insurance Underwriter/Coverage Attorney/Consultant/Professional (Contract)

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. 𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗥𝗼𝗹𝗲 Brain Co is applying frontier AI to specialty insurance. We are building AI that reads specialty insurance policies — declarations, forms, endorsements, schedules of underlying, layered programs — and turns them into structured, decision-ready data. We need experienced specialty insurance underwriters/coverage attorneys/consultants/professionals to help us do it right. You will work directly with our engineering team: telling us where our AI falls short, annotating insurance documents, reviewing AI-generated output, and helping us build the rubrics and benchmarks that define quality for our machine learning pipelines. Your expertise shapes the product so it reflects how specialty insurance is actually practiced. We are looking for someone who can commit approximately 2–10 hours per week for an initial 3–4 months, with the opportunity to extend based on mutual interest and project needs. 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗗𝗼 • 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗲 𝗽𝗼𝗹𝗶𝗰𝘆 𝘁𝗼𝘄𝗲𝗿𝘀. Locate and annotate the limits structure within a given excess policy — limits, sublimits, retentions, attachment points, and how the layers stack. • 𝗥𝗲𝗻𝗱𝗲𝗿 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲 𝗰𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻𝘀. Given an insuring agreement and its ensuing exclusions, produce a structured conclusion about how coverage responds to a described scenario. • 𝗤𝗔 𝗮𝗻𝗱 𝗰𝗼𝗿𝗿𝗲𝗰𝘁 𝗹𝗮𝗯𝗲𝗹𝗶𝗻𝗴. Review AI-generated output, fix mislabeled data, and grade agent performance against what a seasoned practitioner would conclude. • 𝗔𝗱𝗷𝘂𝗱𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗰𝗮𝘀𝗲𝘀. Resolve the ambiguous, conflicting, and unusual situations — manuscript wordings, follow-form excess, nested exclusions, schedule-of-underlying disputes, implicit market conventions — that determine whether a product is trusted by real practitioners. • 𝗕𝘂𝗶𝗹𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗿𝘂𝗯𝗿𝗶𝗰𝘀. Partner with our team to design the scoring criteria and review workflows that tell us, line by line, whether an extraction or a coverage conclusion is accurate. • 𝗦𝗵𝗮𝗽𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿. Help us decide how coverage should be interpreted and presented — what a practitioner needs to see, in what structure, and what would mislead them. Translate practice into product requirements. • 𝗕𝗲 𝗼𝘂𝗿 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗼𝗿. Sit between the documents and the engineers: explain terminology, market convention, and the "why" behind how things are done, and flag where our assumptions do not match reality across lines of business. • 𝗗𝗲𝗳𝗶𝗻𝗲 𝗴𝗿𝗼𝘂𝗻𝗱 𝘁𝗿𝘂𝘁𝗵. Review real policies, endorsements, and claims documents and tell us the correct answer — coverages, limits, sublimits, retentions, attachment points, exclusion logic, and how layers stack — so we can measure