Fire Science Intern

Location

San Francisco

Full time

On-site

Science & Engineering

• $30 per hour

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.

We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.

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.

About Stand Insurance: Stand is a technology and insurance company reimagining how society assesses, mitigates, and adapts to climate risk for homeowners. Our leadership team brings deep experience across insurance, technology, and climate science, having built significant market value at prior ventures.

Traditional insurance models often rely on broad exclusions, leaving many homeowners with limited or no viable coverage options. At Stand, we use deterministic, physics-based models and advanced analytics to deliver personalized risk assessments - empowering homeowners to secure coverage and take proactive, science-driven steps toward resilience.

Why Join Stand: At Stand, you’ll join a mission-driven team redefining insurance through the lens of climate resilience, building a transformative, data-driven insurance model with real-world impact for homeowners and communities on the front lines of climate change.

Background: Most property insurers assess wildfire risk using broad proxies, backward-looking loss data, and simplified hazard scores. While sufficient for portfolio pricing, these tools break down at the property level—where homeowners need to understand what actually drives loss and what actions meaningfully reduce it.

Stand operates from first principles. We simulate fire behavior and structure exposure using deterministic, physics-based models, then validate those models against controlled fire experiments. The result is a shift from correlation-based pricing to a causal understanding of wildfire risk and mitigation effectiveness.

Experiments and simulation validation are, therefore, foundational to our work. Converting experimental results into clean, well-documented, simulation-ready datasets is critical to ensuring our models are accurate, trustworthy, and actionable for underwriting and mitigation decisions.

Location: Onsite in Jackson Square, San Francisco.

Compensation: $30/hr targeting 40/hrs a week. We do not cover relocation or lodging stipends.

Role

Description:

We’re seeking a Fire Science Engineering Intern to support Stand’s fire experimentation and simulation validation efforts. This role spans experiment design, execution, data processing, and numerical simulation tie-out
.

You will help design and run controlled fire experiments, manage instrumentation and data capture, and convert raw experimental outputs into validation-ready datasets that directly support physics-based fire modeling.

This is an immersive, project-based internship designed for students or early-career engineers interested in fire science, experimental methods, and computational modeling—not a survey or observer role.

Core Responsibilities:

• Design and support controlled fire experiments by defining test conditions, configurations, and instrumentation layouts
• Operate experimental instrumentation, DAQ systems, and cameras during live fire tests to ensure complete and synchronized data capture
• Troubleshoot sensor, DAQ, and timing issues under real test-day constraints
• Process raw experimental outputs into clean, reproducible, validation-ready datasets using repeatable workflows
• Support…