Machine Learning Team Lead

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 the MLE Team Lead on the Applied Science team, you will lead the Machine Learning Engineering sub-team as it develops and deploys Stand's flagship AI capabilities spanning physics-informed machine learning, digital twins, computer vision, and spatial intelligence. You will own the technical direction, planning, and execution of critical AI initiatives, ensuring they align with business priorities, ship on schedule, and deliver measurable outcomes.

This is a player-coach role, combining direct technical work and the leadership work around it: people management, project planning, cross-team coordination, and process
. Reporting directly to the Chief Science Officer, you will own key projects yourself while ensuring the broader MLE team is operating effectively, growing, and delivering real impact. You are the person who looks around corners, sees what the business needs, and turns  the business needs X  into  the team builds Y.

You will partner across Applied Science and the business to transform research and emerging technologies into scalable systems that directly influence underwriting, pricing, mitigation, inspection, and customer decision-making.

• Advancing physics-informed, AI-driven solvers and surrogate architectures
• Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities
• Driving R&D programs through to validation, deployment, and business adoption
• Building production-ready AI systems that accelerate, automate, and scale risk analytics

• Lead the Machine Learning Engineering sub-team
  , defining priorities, coordinating execution, and unblocking the team to deliver on critical AI initiatives
• Manage and grow the team
  , running 1-on-1s and growth conversations, giving direct and timely feedback, managing performance, and mentoring engineers as the team scales
• Design, build, and deploy machine learning systems spanning physics-informed AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components
• Own projects end-to-end
  , from problem definition and prototyping through production deployment, adoption, and ongoing performance
• Extend state-of-the-art models and surrogate architectures to accelerate simulation and risk analytics workflows
• Guide, support, and build scalable ML infrastructure
  , including data pipelines, training systems, evaluation frameworks, and production monitoring
• Improve how the team works
  , creating process improvements and maintaining traceability
• Drive cross-functional alignment
  , coordinating across Applied Science and the business and clearly communicating modeling decisions, tradeoffs, and status
• Set a multi-year vision for the MLE team's impact and articulate how its work moves the business

• Proficiency with modern ML tooling and infrastructure
• Experience leading engineers and technical initiatives
  , delivering complex projects through others as well as through direct individual contribution
• Strong…