Applied AI Scientist (PhD)

June 22, 2026

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<h2>ABOUT OUTMARKET</h2>Outmarket is the AI platform for insurance, trusted by more than 250 brokerages to run the work their business depends on. Commercial insurance still runs on dense documents and slow, manual workflows, and that is exactly what we automate: quote comparisons, coverage gap and tower analysis, policy review, and proposal generation, all grounded in our customers’ own data and source-cited so teams can trust the output.The impact is concrete. Teams save 12 to 15 hours per person every week, cut errors by roughly 65 percent, and win more business, all on infrastructure that is SOC 2 Type II certified, single-tenant, and never used to train AI models. We are an AI-first company in both what we build and how we work, shipping quickly and in close partnership with the agencies that rely on us.WHAT YOU’LL GET<ul style="min-height:1.5em"><li>A high-impact role with ownership from day one.</li><li>Competitive compensation and meaningful equity.</li><li>Direct collaboration with founders and real users.</li><li>Remote-first flexibility.</li><li>The opportunity to help build an AI-native product from the ground up.</li></ul><h2>ABOUT THE ROLE</h2>We are hiring an Applied AI Scientist to push the frontier of what is possible with LLMs, NLP, and machine learning in real-world environments. This is not a role for someone who wants to optimize benchmarks in isolation and hand work off to someone else. It is a role for someone who wants to see advanced research become customer-facing product quickly.You will work closely with founders, product leaders, and engineers to turn advanced AI techniques into systems that operate under real production constraints.<h2>WHY THIS ROLE</h2><ul style="min-height:1.5em"><li>Apply cutting-edge AI research to live customer workflows and production systems.</li><li>Work directly with founders and a highly experienced technical team.</li><li>Build solutions for document intelligence, quote comparison, workflow optimization, and other high-value use cases.</li><li>See your work move from research to deployment in weeks, not quarters.</li><li>Help define how applied AI creates advantage in a large, underserved market.</li></ul><h2>WHAT YOU’LL DO</h2><ul style="min-height:1.5em"><li>Research and develop LLM-based solutions for NLP, document intelligence, semantic search, and data extraction.</li><li>Design and improve prompt strategies, retrieval-augmented generation systems, and fine-tuned models.</li><li>Partner with product and engineering teams to bring AI capabilities into customer-facing workflows.</li><li>Build evaluation pipelines and benchmarks for accuracy, performance, and robustness.</li><li>Stay current on new research and rapidly test promising techniques in practical settings.</li><li>Operate as both a scientist and builder, with direct responsibility for whether ideas survive contact with real data.</li></ul><h2>WHAT WE’RE LOOKING FOR</h2><ul style="min-height:1.5em"><li>PhD in Computer Science, Machine Learning, NLP, or a related field.</li><li>Strong research background with publications in top-tier AI venues such as NeurIPS, ACL, ICML, or EMNLP.</li><li>Hands-on experience with LLMs, transformers, embeddings, or neural information retrieval.</li><li>Proficiency in Python and ML tooling such as PyTorch, Hugging Face, and LangChain.</li><li>Track record of applying research in practical, production-oriented systems.</li><li>Strong technical judgment about what is novel, what is useful, and what is actually ready to ship.</li></ul><h2>BONUS IF YOU HAVE</h2><ul style="min-height:1.5em"><li>Experience with unstructured data such as PDFs, forms, contracts, or portals.</li><li>Background in enterprise or B2B AI applications.</li><li>Familiarity with insurance, legal, or document-heavy industries.</li></ul><h2>WHAT YOU’LL GET</h2><ul style="min-height:1.5em"><li>A high-impact role on a team that moves quickly and builds with purpose.</li><li>Competitive compensation and meaningful equity.</li><li>Remote-first flexibility.</li><li>Access to real data, real users, and fast feedback cycles.</li><li>The opportunity to bring advanced research to life in a major industry.</li></ul>

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