Machine Learning Engineer

📋 Job Overview

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.

🏢 About Stand

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.

🎯 The Role

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.

✅ Key Responsibilities

• 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

📌 Required Qualifications

• Deep hands-on experience designing and training multimodal models, fusing heterogeneous data (e.g., 3D/vision, simulation outputs, tabular, and text) into shared representations; strong VLM exposure
• Experience building and post-training multi-step LLM agents that call external tools and interact with environments, including evaluating if a trajectory reflects appropriate judgment and user constraints
• 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
• Strong project ownership and execution: planning, prioritization, and delivery of complex technical work
• Ability to operate across disciplines, connecting technical development to business objectives
• Strong, succinct communication and judgment to balance R&D, delivery timelines, and business impact
• Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments

⭐ Desirable Experience

• Experience with embodied AI— training agents to navigate in 3D environments
• Experience with retrieval and embedding systems: vector search and similarity in latent space
• Experience with geometric deep learning: point clouds, meshes, and spatially-aware architectures
• Familiarity with physics-informed AI and surrogate modeling across a number of domains
• Experience in startups or zero-to-one technology development
• Knowledge of geospatial, remote sensing, or Earth observation datasets

🎁 Benefits

• Above-market Health, Dental, and Vision coverage
• Weekly lunch stipend
• Flexible time off + holidays
• 401(k) plan
• Commuter benefits
• PAT & MAT Leave
• Short-Term and Long-Term Disability Insurance

🛂 Visa & Eligibility

Not specified