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You will collaborate with engineering teams to annotate complex insurance policy documents and define rubrics for AI model training. Your role involves reviewing AI-generated outputs to ensure accuracy and translating insurance market conventions into actionable product requirements.
Rebuild how the world works, to make institutions work better for the people they serve.
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.
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. Itโs the kind of work you'll still be proud of in ten years from now.
๐๐ฏ๐ผ๐๐ ๐๐ต๐ฒ ๐ฅ๐ผ๐น๐ฒ
Brain Co is applying frontier AI to specialty insurance. We are building AI that reads specialty insurance policies โ declarations, forms, endorsements, schedules of underlying, layered programs โ 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 2โ10 hours per week for an initial 3โ4 months, with the opportunity to extend based on mutual interest and project needs.
๐ช๐ต๐ฎ๐ ๐ฌ๐ผ๐ ๐ช๐ถ๐น๐น ๐๐ผ
โข ๐๐ป๐ป๐ผ๐๐ฎ๐๐ฒ ๐ฝ๐ผ๐น๐ถ๐ฐ๐ ๐๐ผ๐๐ฒ๐ฟ๐. Locate and annotate the limits structure within a given excess policy โ limits, sublimits, retentions, attachment points, and how the layers stack.
โข ๐ฅ๐ฒ๐ป๐ฑ๐ฒ๐ฟ ๐ฐ๐ผ๐๐ฒ๐ฟ๐ฎ๐ด๐ฒ ๐ฐ๐ผ๐ป๐ฐ๐น๐๐๐ถ๐ผ๐ป๐. Given an insuring agreement and its ensuing exclusions, produce a structured conclusion about how coverage responds to a described scenario.
โข ๐ค๐ ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐ ๐น๐ฎ๐ฏ๐ฒ๐น๐ถ๐ป๐ด. Review AI-generated output, fix mislabeled data, and grade agent performance against what a seasoned practitioner would conclude.
โข ๐๐ฑ๐ท๐๐ฑ๐ถ๐ฐ๐ฎ๐๐ฒ ๐๐ต๐ฒ ๐ต๐ฎ๐ฟ๐ฑ ๐ฐ๐ฎ๐๐ฒ๐. Resolve the ambiguous, conflicting, and unusual situations โ manuscript wordings, follow-form excess, nested exclusions, schedule-of-underlying disputes, implicit market conventions โ that determine whether a product is trusted by real practitioners.
โข ๐๐๐ถ๐น๐ฑ ๐ฒ๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป ๐ฟ๐๐ฏ๐ฟ๐ถ๐ฐ๐. 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.
โข ๐ฆ๐ต๐ฎ๐ฝ๐ฒ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐ ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ. Help us decide how coverage should be interpreted and presented โ what a practitioner needs to see, in what structure, and what would mislead them. Translate practice into product requirements.
โข ๐๐ฒ ๐ผ๐๐ฟ ๐๐ฟ๐ฎ๐ป๐๐น๐ฎ๐๐ผ๐ฟ. 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.
โข ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐ด๐ฟ๐ผ๐๐ป๐ฑ ๐๐ฟ๐๐๐ต. Review real policies, endorsements, and claims documents and tell us the correct answer โ coverages, limits, sublimits, retentions, attachment points, exclusion logic, and how layers stack โ so we can measure and improve the AI against an expert standard.
๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐ฑ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ
โข USA-based. 3 years of experience minimum; 5+ years strongly preferred, specifically within excess and specialty insurance.
โข Experience in excess and specialty (E&S) insurance as an underwriter, coverage attorney or counsel, or consultant.
๐ช๐ต๐ผ ๐๐ฒ'๐ฟ๐ฒ ๐น๐ผ๐ผ๐ธ๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ
โข Have deep, hands-on experience in specialty insurance โ underwriting, claims, broking, or coverage counsel โ and/or broad multiline experience across the lines we work in (General Liability, Professional Liability, and related casualty and specialty lines).
โข Can read a policy end-to-end โ declarations, forms, endorsements, schedules of underlying โ and explain precisely how coverage responds, including across primary and excess layers.
โข Have strong opinions on what "correct" means and can articulate the reasoning, not just the verdict, so it can be encoded into an AI system.
โข Communicate clearly with non-insurance experts and enjoy the back-and-forth of teaching engineers your craft.
โข No technical background required - your job is to tell us if the AI gets it right, not to explain how it works
๐ก๐ถ๐ฐ๐ฒ ๐๐ผ ๐๐ฎ๐๐ฒ
โข Claims experience โ working as, or closely alongside, claim adjusters, claim examiners, complex or litigated claims specialists, or claims counsel.
๐๐ผ๐บ๐ฝ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป & ๐๐ผ๐ด๐ถ๐๐๐ถ๐ฐ๐
โข Part-time, contract engagement with flexible scheduling that fits your current work.
โข Competitive hourly rate commensurate with experience and depth of expertise.
โข Remote-friendly, with collaboration over shared documents and working sessions with our AI and product teams.
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