Machine Learning Engineer - Multimodal Modeling

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, Policy Genius, WePay, and Hotel Tonight, bringing deep experience in building and scaling high-growth companies.

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.

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‑ownership position on the Machine Learning team within Stand Applied Science. You will own modeling work end‑to‑end, from architecture and training strategy through evaluation and production deployment, and partner closely with the Platform team to ensure the agentic harness and workflows your models plug into deliver strong results in production.

Your partnerships will extend across the business, mirroring the breadth of the model's inputs: collecting technical insight from subject matter experts and other MLEs, and institutional judgment from underwriting, pricing, mitigation, inspection, and customer decision‑making.

• Designing and training multimodal model architectures that jointly reason over physical, spatial, and business‑context data

• Building frameworks that let these models act as agents within nuanced workflows, making complex tool calls that include interacting with our world‑modeling stack

• Developing retrieval and similarity capabilities over learned representations of real‑world assets and their multi‑layered complexities

• Standing up the training‑data pipelines, evaluation harnesses, and production monitoring that take these models from prototype to production, collaborating closely with Applied Science infrastructure engineers to build out proper tooling

• Design, build, and deploy machine learning systems spanning multimodal learning, physics‑informed AI, digital twins, and spatial intelligence, contributing directly to core business impact

• Own projects end‑to‑end, from problem definition and prototyping through production deployment, adoption, and ongoing performance monitoring

• Develop rigorous evaluation frameworks that weigh model judgments against real business outcomes

• Build on and extend scalable ML infrastructure

• Partner with Stand’s Platform team on the model‑harness interface

• Drive cross‑functional alignment, communicating decisions, tradeoffs, and status

• Deep hands‑on experience designing and training multimodal models, fusing heterogeneous data (e.g., 3D/vision, simulation outputs, tabular, and text) into shared representations

• A record of bringing models of this class to production: training at scale, evaluation, deployment, and iteration on live systems

• Experience applying ML to complex physical systems. We are agnostic to the domain: atmospheric, molecular, protein, robotics, fluid dynamics, or other physics‑grounded modeling all carries over

• Experience training or fine‑tuning LLMs, including tool use,…