Frontend Visualization Engineer

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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 https://frontier.standinsurance.com/ . We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices. Leadership: 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: On the Applied Science team, we build state-of-the-art digital twins to model and mitigate catastrophic risk. Our goal is to turn advanced physics simulations and AI-driven analytics into intuitive tools that empower users to understand, interact with, and act on risk. As a Frontend Visualization Engineer , you'll be the bridge between complex models and real-world usability. Your primary focus will be designing and shipping front-end tools that make simulations intuitive and performant -collaborating closely with Product, Engineering, and Applied Science in a highly cross-functional environment. You'll play a critical role in connecting our deep-tech capabilities (e.g., CFD, FEA, AI models) with end users, enabling non-technical audiences to visualize, manipulate, and run their own simulations-transforming how catastrophe-induced risk is assessed. The sweet spot for this role is strong engineering discipline in building and shipping production-ready tools for simulation workflows, paired with the ability to create intuitive model interfaces . Experience with 3D visualization and rendering physics outputs is a plus, not a must-have. Beyond that, we're looking for someone who is excited to grow into product and design depth -bringing product intuition and shaping workflows over time, even if not fully proven on day one. This Role Will: Own front-end tooling for the Applied Science team, from design through production Partner with Product to evolve deep-tech into fully supported features aligned with user needs Build efficient, intuitive interfaces to visualize, edit, and label digital twins in our simulation pipelines Collaborate across teams to transform CFD and AI outputs into actionable visualizations Integrate diverse data sources (e.g., LiDAR, drones, ground-data), capturing real-world changes Leverage off-the-shelf ML models to accelerate simulation prep and property assessments Core Skills (Must-Haves): UX : design intuitive interfaces for digital twins (user flows, interaction patterns, affordances) Front-end expertise in TypeScript + React (shipping, owning and maintaining production tools) 3D / technical visualization: build and optimize interactive scenes (Three.js/R3F), e.g., scene graphs, selection, transforms, annotations, and performance tuning Modern software practices : comfortable with standardized dev environments (monorepos/containers), CI/CD pipelines, automated testing, code reviews, observability, and shared libraries Product sense & collaboration : partner with Product to translate prototypes into supported features; align requirements; and communicate clearly in demos and docs Industry experience & degree in relevant field. Additional Skills For Success (Helpful, Not Required): Geospatial/3D data wrangling : point clouds/meshes, LiDAR & orthomosaics, 2D masks/segmentations; coordinate systems; integrating sources of varying accuracy and vintage Exposure to CFD/FEA/physics outputs (e.g., combustion, wind) and visualizing scientific data General ML literacy familiarity with integrating off-the-shelf models/segmentations and reasoning about basic metrics (on-the-job learning encouraged) Geospatial ecosystem familiarity

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