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Machine Learning Engineer

Stand Insurance

San Francisco, CAOn-siteFull-time$250k – $295k / year

About this role

Machine Learning Engineer - Multimodal Modeling @ Stand Insurance - Back to Stand Insurance’s Job Listings Machine Learning Engineer - Multimodal Modeling Location San Francisco Employment Type Full time Location Type Hybrid Department Science & Engineering Compensation - $250K – $295K • Offers Equity Why Join Stand: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our Stand World Model. We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.Our leadership team includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies. Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable. Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction. Role Summary: As a Machine Learning Engineer on the Applied Science team, you will design, train, and deploy Stand's flagship AI capabilities, with a central focus on the multimodal meshing of our Stand World Model with powerful language models. This work brings physical simulation, rich 3D representations of real assets, and broader business context together into models that can reason across all of them at once, in support of better underwriting, pricing, and mitigation decisions. This is a hands-on, high-

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